Abstract

Introduction

Children with genu varus needs frequent assessment and follow up that may need several radiographies. This study investigates the effectiveness of the clinical assessment of genu varus in comparison to the radiological assessment.

Methods

In this study, relationship between clinical and radiographic assessments of genu varus (bow leg) in children, focusing on the use of intercondylar distance (ICD) and clinical tibiofemoral angle (cTFA) as clinical measures, compared to the mechanical tibiofemoral angle (mTFA) obtained via scanogram, the radiographic gold standard for assessing lower limb deformity. Clinical measurements (ICD and cTFA) were gathered along with the mTFA from scanogram radiographs. Reliability was tested between two observers, and Spearman’s correlation coefficient was used to evaluate the relationships between the clinical and radiographic measurements.

Results

The study involved 36 children with an average age of 6.3 years. There were strong intra-rater reliability for both observers (ICC 0.87 for observer 1, ICC 0.97 for observer 2) and excellent inter-observer agreement (ICC 0.97). Positive correlations were found between cTFA and mTFA (r² = 0.67, p < 0.001), between ICD and cTFA (r² = 0.53, p < 0.001), and between ICD and mTFA (r² = 0.62, p < 0.001).

Conclusion

This study suupports the idea that clinical methods may be sufficient for evaluation, minimizing the need for radiation exposure and offering a reliable alternative to radiography.

Introduction

Genu varus, also known as bow-leggedness is defined as any separation of the medial surfaces of the knees when the medial malleoli are in contact, and the patient is standing in the anatomical position [1]. The prevalence of genu varus ranges from 11.4% to 14.5% [2,3]. It is found to be more prevalent in boys than in girls [2]. Genu varus may be physiological or pathological. There are multiple ways to aid in the screening and diagnosis of genu varus, which include clinical and radiological methods. Clinical methods such as intercondylar distance (ICD) and tibiofemoral angle measurement have been used to screen and assess the degree of genu varus. However, imaging modality such as a long-leg AP radiograph or scanogram is considered the gold standard assessment for lower limb deformity.

Many studies on genu varus in children have utilized either the clinical or radiological lower limb measurements to describe the tibiofemoral angle progression in normal children, data of normal ranges of knee angle in relation to age, and transition time from varus to valgus of different populations and ethnic groups [4-10].In a recent systematic review, it is proposed that children above the age of 18 months with genu varus should be closely monitored clinically using ICD or cTFA, whereby an ICD of more than 4 cm needed to be investigated for pathologic cause [11]. However, reliability has not been confirmed. 

Hence, serial assessment might be needed to manage children with genu varus. Clinical methods of assessment are preferrable due to no exposure to radiation as compared to a radiograph but may be inaccurate or unreliable [12]. We are interested to find out the correlation between the radiological and clinical assessments.

Methods

Study design and setting

This was a single center cohort study. The study was conducted in an orthopaedic clinic of a tertiary hospital. Children with age ranging from 1 to 17 years old who were diagnosed as genu varus by orthopaedic specialists and has long leg radiograph done, were included. We excluded children who have previous history of fracture of the lower limb, had any knee swelling, tumour or contracture. Consent was taken from the parents before enrolment to the study. This study was conducted in accordance with the Declaration of Helsinki and was approved by the Universiti Kebangsaan Malaysia Institutional Ethical Committee (JEP-2020-194).

Procedure

The baseline data such as age, gender, weight/height and underlying diagnosis were taken. The knee intercondylar distance was measured using a measuring tape, with the child standing, and both medial malleoli touching. The centre of the medial femoral condyles was identified by palpation of the most prominent part of the distal femur. The measurement between the condyles was performed following the method described by Heath et al [13]. The reading was measured in centimetres as the intercondylar distance. The clinical tibiofemoral angle (cTFA) was measured with a goniometer, following the method described by Arazi et al [14]. With the child in standing, the anterior superior iliac spine, centre of the patella, and midpoint of the ankle joint were marked with a pen. After the marking of the tibiofemoral axis, the angle was measured and recorded. The angle was expressed in degrees. Illustrates the method of measurements on a patient (Figure 1).

A standardized long-leg anterior-posterior radiograph (scanogram) of lower limbs was obtained from hospital radiological database. The angle formed between the mechanical axis of the femur and the mechanical axis of tibia was recorded as mechanical tibiofemoral angle (mTFA). The mTFA was determined from digital X-Ray by using the measuring tool from Medweb (Medweb, Inc, San Francisco, CA) software. In bilateral cases, the limb with the worst angle measured was chosen for analysis.

The clinical and radiological measurements were performed by a single researcher (CYT), who was trained on the measurement technique. For the radiographic measurements, a prior intra- and inter-observer reliability study was performed on 10 radiographs by two main researchers (CYT and KJ) on the same children at two different intervals.

Data analysis

The intra- and inter-observer reliability of tibiofemoral angle measurement was measured using with 95% confidence intervals to gauge the precisions of the ICCs [15]. Correlations between clinical tibiofemoral angle (cTFA), mechanical tibiofemoral angle (mTFA) and intercondylar distance (ICD) were tested using Spearman’s Correlation test. Differences between cTFA and mTFA were investigated using paired sample t-test and Bland Altman 95% limits of agreement. All statistical analysis was performed using SPSS (v24, IBM, NY, USA). Statistical significance was set at a cut-off of p<0.05.

Results

There were 36 children included with the mean age of 6.3 years. Thirty-two were Malay (88.8%), while the remaining participants were three Indians (8.3 %) and one Chinese (2.7%) by ethnicity. Twenty-two children were male (61%) and 14 female (38%). There were five unilateral and 31 bilateral genu varus. Eleven children had Blount disease; 13 cases had rickets while the remaining 12 was managed as physiological genu varus.

Reliability study performed between two observers for the tibiofemoral angle measurements revealed Good intra-rater reliability for observer 1 (ICC 0.87) and Excellent intra-rater reliability for observer 2 (ICC 0.97). Excellent inter-observer agreement (ICC 0.97) was also shown.

All thirty-six children (mean age 6.6 ± 5.7) were examined in standing position. The association between the radiological mTFA and clinical TFA measurements was assessed. Our findings revealed that there was a moderate correlation between cTFA and mTFA (r²=0.67, p< 0.001) (Figure 2).

Subsequently, the association between the ICD and clinical TFA and between ICD and radiological mTFA measurements were assessed. We also found a moderate positive correlation between ICD and cTFA, (r²=0.53, p< 0.001) and between ICD and mTFA, (r²=0.62, p< 0.001), respectively (Figure 3).

Paired t- test revealed a mean difference of -4.67 degrees between the cTFA and mTFA. The difference was statistically significant of p= 0.00. The limits of agreement revealed were a lower limit of -7.02 degrees and an upper limit of -2.34 degrees (Figure 4).

Discussion

We examined the correlation between clinical and radiographic TFA measurements of the lower extremities in 36 children with genu varus who has been referred to our centre. We found a significant correlation between radiological mTFA and clinical TFA. This result is in parallel with other studies by [16,17].  Navali et al concluded that goniometer measurement appears to be valid alternatives to the mechanical axis on full-leg radiograph for determining frontal plane knee alignment [17].  Kraus et al also concluded knee alignment assessed clinically by goniometer or measured on a knee radiograph is correlated with the angle measured on the full-limb radiograph [17]. However, both studies were carried out in adults’ population with osteoarthritis knee. Our study determined the correlation between radiological and clinical TFA specifically in paediatric population with genu varus.

Another significant finding in this study is ICD has moderate correlation with cTFA and mTFA. There are several correlation studies that were reported on ICD. Saini et al found that a fair degree of correlation was established between ICD and tibiofemoral angle (TFA), measured clinically by a goniometer [8]. A similar finding between ICD and TFA was seen in other studies [6-11]. This suggested that both measurements can complement each other in monitoring genu varus. The importance of ICD measurement was highlighted by other authors. Cahuzac et al in 1995 has established a data for the normal values of varus profile of the legs in normal children between 10 and 16 years of age, whereby a measurement of ICD of more than 5 cm is considered abnormal [18]. This is supported by other investigators [14-19]. For younger children aged of at least 18 months, ICD of 4cm should be closely monitored [11].

The different degrees of correlation in various studies might be influenced by the different method of measurements. Mathew et al had found the clinical measurement of using ICD to have minimal intra-observer variability [6]. However, a standardized way of measurement and positioning of the patients is important to get a consistent finding. Obtaining a proper standing radiograph in a young child can proved to be challenging, so other measures such as footprint drawn on the floor have been suggested [11].

We also found that the difference of agreement between cTFA and mTFA measurement were significant. mTFA consistently produced a higher value with the mean difference around 5 degrees indicating that the angles were not similar between the two techniques. However, as mentioned earlier both measurements correlated with each other. This means that although not totally accurate as measured on radiograph (mTFA), clinical method can still show similar trend of deformity therefore useful for monitoring change or progress.

There were some limitations in our study. Firstly, our sample population was relatively small with a wide age range (1-17 years).  Secondly, we only performed observer reliability study for the radiographic measurement. However, the clinical measurements were done by a single researcher, who was trained to perform the measurement following the standard protocol.

Conclusion

Clinical measurement of tibiofemoral angle and ICD to good correlation with radiological measurement, when performed with the child in standing position. Therefore, for monitoring purposes or serial alignment assessment, these methods are adequate.

Declarations

Conflicts of interest: The author(s) have no conflicts of interest to disclose.

Ethical approval: The study's ethical approval was obtained from the Universiti Kebangsaan Malaysia Institutional Ethical Committee (JEP-2020-194).

Patient consent (participation and publication): Verbal informed consent was obtained from patients for publication.

Source of Funding: Universiti Kebangsaan Malaysia

Role of Funder: The funder remained independent, refraining from   involvement   in   data   collection, analysis, or   result formulation, ensuring unbiased research free from external influence.

Acknowledgements: None to be declared.

Authors' contributions: KJ and CYT conceptualized and designed the study, drafted the initial manuscript, and reviewed and revised the manuscript. CYT designed the data collection instruments, collected data and carried out the initial analyses. AFAR and AHAR coordinated and supervised data collection, and critically reviewed the manuscript for important intellectual content. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

Use of AI: AI was not used in the drafting of the manuscript, the production of graphical elements, or the collection and analysis of data.

Data availability statement: Note applicable.

Abstract

Introduction

The exact manner in which large language models (LLMs) will be integrated into pathology is not yet fully comprehended. This study examines the accuracy, benefits, biases, and limitations of LLMs in diagnosing dermatologic conditions within pathology.

Methods

A pathologist compiled 60 real histopathology case scenarios of skin conditions from a hospital database. Two other pathologists reviewed each patient’s demographics, clinical details, histopathology findings, and original diagnosis. These cases were presented to ChatGPT-3.5, Gemini, and an external pathologist. Each response was classified as complete agreement, partial agreement, or no agreement with the original pathologist’s diagnosis.

Results

ChatGPT-3.5 had 29 (48.4%) complete agreements, 14 (23.3%) partial agreements, and 17 (28.3%) none agreements. Gemini showed 20 (33%), 9 (15%), and 31 (52%) complete agreement, partial agreement, and no agreement responses, respectively. Additionally, the external pathologist had 36(60%), 17(28%), and 7(12%) complete agreements, partial agreements, and no agreements responses, respectively, in relation to the pathologists’ diagnosis. Significant differences in diagnostic agreement were found between the LLMs and the pathologist (P < 0.001).

Conclusion

In certain instances, ChatGPT-3.5 and Gemini may provide an accurate diagnosis of skin pathologies when presented with relevant patient history and descriptions of histopathological reports. However, their overall performance is insufficient for reliable use in real-life clinical settings.

Introduction

The healthcare sector is undergoing significant transformation with the emergence of large language models (LLMs), which have the potential to revolutionize patient care and outcomes. In November 2022, OpenAI introduced a natural language model called Chat Generative Pre-Trained Transformer (ChatGPT). It is renowned for its ability to generate responses that approximate human interaction in various tasks. Gemini, developed by Google, is a text-based AI conversational tool that utilizes machine learning and natural language understanding to address complex inquiries. These models generate new data by identifying structures and patterns from existing data, demonstrating their versatility in producing content across different domains. Generative LLMs rely on sophisticated deep learning methodologies and neural network architectures to scrutinize, comprehend, and produce content that closely resembles human-created outputs. Both ChatGPT and Gemini have gained global recognition for their unprecedented ability to emulate human conversation and cognitive abilities [1-3].

ChatGPT offers a notable advantage in medical decision-making due to its proficiency in analyzing complex medical data. It is a valuable resource for healthcare professionals, providing quick insights derived from patient records, medical research, and clinical guidelines [1,4]. Moreover, ChatGPT can play a crucial role in the differential diagnostic process by synthesizing information from symptoms, medical history, and risk factors, and comprehensively processing this data to present a range of potential medical diagnoses, thereby assisting medical practitioners in their assessments. This has the potential to improve diagnostic accuracy and reduce instances of misdiagnosis or delays [4].

The integration of ChatGPT and Gemini into the medical decision-making landscape has generated interest from various medical specialties. Multiple disciplines have published articles highlighting the significance and potential applications of ChatGPT and Gemini in their respective fields [2,5]. Despite the growing number of these models used in diagnostics, patient management, preventive medicine, and genomic analysis across medicine, the integration of LLMs in dermatology remains limited. This study emphasizes the exploration of large language models, highlighting their less common yet promising role in advancing dermatologic diagnostics and patient care [6]

This study aims to explore the role of LLMs and its decision-making capabilities in the field of pathology, specifically in dermatologic conditions. It focuses on ChatGPT 3.5 and Gemini and compares their accuracy and concordance with the diagnoses of human pathologists. The study also investigates the potential advantages, biases, and constraints of integrating LLM tools into pathology decision-making processes.

Methods

Case Selection

A pathologist selected 60 real case scenarios, with half being neoplastic conditions and the other half non-neoplastic, from a hospital’s medical database. The cases involved patients who had undergone biopsy and histopathological examination for skin conditions. The records included information on age, sex, and the chief complaint of the patients, in addition to a detailed description of the histopathology reports (clinical and microscopic description without the diagnosis).

Consensus Diagnosis

Two additional board-certified pathologists reviewed each case, reaching a collaborative consensus diagnosis through a meticulous review of clinical and microscopic descriptions. This process ensured diagnostic accuracy and reliability while minimizing individual biases.

Eligibility Criteria

The study included cases that had complete and relevant histopathological reports and comprehensive patient demographic information. Specifically, cases were included if they provided a definitive diagnosis in the histopathological report and contained detailed patient data such as age, gender, and clinical history. Cases were excluded if the histopathological report was incomplete, lacked critical patient information, or if the diagnosis could not be definitively made based solely on the textual description.

Sampling Method

The selection process involved a systematic review of available cases from the hospital's medical database to ensure a representative sample of different dermatologic diagnoses. A random sampling method was employed to minimize selection bias and to ensure the sample was representative of the broader population of dermatologic conditions within the database. The selected cases span a range of common and less common dermatologic conditions, enhancing the generalizability of the study’s findings.

Evaluation by AI Systems and External Pathologist

In March 2023, these cases were evaluated using two LLM systems, namely ChatGPT-3.5 and Gemini. In addition, an external board-certified pathologist was tested similarly to the AI systems, receiving only the necessary histopathology report descriptions (without histopathological images) to ensure a fair comparison between the LLM systems and the external pathologist.

Pathologists’ Experience

The Pathologists involved in the study had a minimum of eight years of experience in their respective specialties, handling an average of 30 cases per month. This level of experience ensured a deep familiarity with a wide range of case scenarios. Crucially, the pathologists conducted their assessments were fully informed of the study design, including the comparative analysis with AI systems. Their expertise and understanding were vital in upholding the integrity and reliability of the diagnostic evaluations throughout the study

AI Prompting Strategy

The LLM systems were initially greeted with a prompt saying “Hello,” followed by standardized inquiries presented as: “Please provide the most accurate diagnoses from the texts that will be given below.” Each case was individually presented by copy-pasting it from a Word document and requesting each system to provide a diagnosis of the case scenario based on the information presented. The first response of each system to the inquiry was documented. If no diagnosis was given, the prompt was repeated as such: “Please, based on the histopathological report information given above, provide the most likely disease that causes it.” Until a diagnosis was obtained. In some cases, after a diagnosis was provided, an additional question was asked to specify the histologic subtype of the condition (e.g., if the diagnosis was “seborrheic keratosis”, the system was asked to specify the histologic subtype). Furthermore, the board-certified external pathologist was tested with the same questions, and the correct diagnosis was inquired.

Response Categorization

The responses from both systems and the external pathologist were categorized into three subtypes: complete agreement with the original diagnosis by the human pathologists, partial agreement, or none agreement. The criteria for categorizing agreement levels into "complete," "partial," and "none agreement" are based on the distinction between general and specific diagnostic classifications. For instance, when the original diagnosis provides a detailed type and subtype (e.g., "Seborrheic keratosis, irritated type"), an AI tool's or external pathologist's response was classified as demonstrating "complete agreement" if it accurately identifies both the general diagnosis ("Seborrheic keratosis") and the specific subtype ("irritated type"). This classification acknowledges that accurate identification of both components reflects a thorough understanding and alignment with the original diagnosis. Conversely, an assessment was categorized as "partial agreement" if the response correctly identifies the general diagnosis but inaccurately specifies the subtype. Furthermore, a diagnosis was classified as demonstrating "no agreement" when both the general diagnosis and subtype provided by the AI tool or external pathologist are incorrect. These classification criteria draw upon established methodologies in diagnostic agreement studies, emphasizing the importance of distinguishing between different levels of agreement based on the precision and correctness of diagnostic outputs [7].

Data Processing and Statistical Analysis

The initial processing of the acquired data involved several steps before statistical analysis. First, the data were inputted into Microsoft Excel 2019. Subsequently, they were transferred to Statistical Package for the Social Sciences software (SPSS) 27.0 and the DATA tab for further analysis. Fleiss kappa was utilized to measure agreement among Chat GPT, the external pathologist, and Gemini. Additionally, Chi-square tests were applied to investigate associations between the two LLMs and the external pathologist. In this study, significance was defined as a p-value of < 0.05. A literature review was performed for the study, selectively considering papers from reputable journals while excluding those from predatory sources based on established criteria [8].

Results

ChatGPT-3.5 provided 29 (48.4%) complete agreement, 14 (23.3%) partial agreement, and 17 (28.3%) none agreement responses for the scenarios presented. In contrast, Gemini offered 20 (33%), 9(15%), and 31 (52%) complete agreement, partial agreement, and none agreement responses, respectively, for the same scenarios. Moreover, the external pathologist provided 36 (60%) complete agreement, 17 (28%) partial agreement, and 7 (12%) none agreement responses (Table 1). The complete details of the scenarios, including the diagnosis from the pathologists, ChatGPT’s, Gemini’s, and the external pathologist diagnoses are available in (Supplement 1).

The agreement between Chat GPT, the external pathologist, and Gemini was assessed using Fleiss' kappa, which indicated a statistically significant at a level of <0.001, demonstrating slight to moderate agreement with respect to the original diagnosis made by the pathologists. Out of the 29 questions where Chat GPT agreed with the original diagnosis, only 12 (41.4%) instances also received complete agreement from both Gemini and the external pathologist (Table 2).

When assessing the agreement between Chat GPT, the external pathologist, and Gemini, using the external pathologist as the reference, the external pathologist showed complete agreement with the original diagnosis in 36 cases. Among these, Chat GPT achieved complete agreement in 19 cases (52.7%), while Gemini achieved complete agreement in 15 cases (41.7%). Additionally, the external pathologist showed none agreement with the original diagnosis in only 7 cases. Among these, Chat GPT achieved none agreement in 5 cases (71.4%), while Gemini achieved none agreement in 6 cases (85.7%). Statistical analysis indicated significant differences in agreement levels between AI tools (ChatGPT and Gemini) and the external pathologist, with a P-value of <0.001 (Table 3). 

In addition, the agreement between the external pathologist, ChatGPT, and Gemini was assessed for both neoplastic and non-neoplastic cases. Statistical analysis revealed significant differences in the agreement levels between the LLMs and the external pathologist, with a P-value of <0.001, highlighting the statistically significant disparity in agreement rates between the AI tools and the external pathologist (Table 4 and 5).

Discussion

Despite being in existence for over five decades, LLM has recently garnered substantial attention in the public sphere. The increased focus on LLMs in the medical field has led to speculation about the potential replacement of doctors by these systems. However, LLMs are more likely to serve as a complementary tool, aiding clinicians in efficiently processing data and making clinical decisions. This is substantiated by the fact that LLMs can "learn" from extensive collections of medical data. Modern systems are also noted for their self-correcting capabilities. As electronic medical records become more prevalent, there is a growing reservoir of stored patient data. While having access to more data is undoubtedly advantageous, scanning through patient charts can be challenging. Algorithms have been developed to sift through patient notes and detect individuals with specific risk factors, diagnoses, or outcomes. This capability is particularly valuable because, in theory, a LLM system could be developed to review and extract data from medical charts, including pathology reports, and promptly identify patients at highest risk for conditions that could cause significant morbidity or mortality if missed by the physician [6,9].

The field of pathology is no exception to the adaptation of LLMs and the utilization of these technological advancements. Various in recent years have assessed LLM’s accuracy, potential use, and associated limitations. For instance, a study by Vaidyanathaiyer et al., evaluated ChatGPT's proficiency in pathology through thirty clinical case scenarios. These cases were evenly distributed across three primary subcategories: hematology, histopathology, and clinical pathology, with ten cases from each category. The researchers reported that ChatGPT received high grade of “A” on nearly three-quarters of the questions; in the remaining questions, and “B” grades on remaining questions. They found that ChatGPT demonstrated moderate proficiency in these subcategories, excelling in rapid data analysis and providing fundamental insights, though it had limitations in generating thorough and elaborate information [10]. Furthermore, Passby et al. demonstrated capacity of ChatGPT to address multiple-choice inquiries in the Specialty Certificate Examination of dermatology, with ChatGPT-4 outperforming ChatGPT-3.5, scoring 90% versus 63%, respectively, compared to an approximate passing score of 70% [11]. In an investigation by Delsoz et al., twenty corneal pathologies with their respective case descriptions were provided to ChatGPT-3.5 and ChatGPT-4. ChatGPT-4 performed better, correctly answering 85% of the questions, whereas ChatGPT-3.5 answered only 60% correctly [12]. The current study found that ChatGPT-3.5 performed similarly in the percentage of correct responses. However, this study further evaluated the LLM responses and found that nearly 23.3% and 15% of ChatGPT and Gemini answers, respectively, were fair but still had inaccuracies. This highlight areas where these systems can improve, as they sometimes almost answer correctly but not fully. For instance, when a histopathology report of squamous cell carcinoma in situ was given to ChatGPT-3.5, it answered with squamous cell carcinoma. On further prompting, the system favored an invasive squamous cell carcinoma over an in-situ one, even when the suggestion was made to it whether an in-situ lesion was more appropriate for that scenario. similarly, in the case of guttate psoriasis, Gemini answered with only “psoriasis” did not specify the type, while ChatGPT-3.5 responded with “psoriasis vulgaris”. In a study by Rahsepar et al. on pulmonary malignancies, Google Bard (the former name of Gemini) provided 9.2% partially correct answers, similar to Gemini's 15% partially correct responses in this study. However, ChatGPT-3.5 answered 17.5% of lung cancer questions incorrectly, whereas in the present study, ChatGPT-3.5’s incorrect answers were nearly twice as frequent. This may be due to ChatGPT broader access to data and medical information on lung cancer compared to the dermatological conditions tested in this study, highlighting the limitations and risks of relying on these systems for rarer diseases [13].

Although existing language models have access to extensive medical data, they often lack a nuanced understanding of individual diseases or specific patient cases. They have not undergone specialized training for medical tasks, relying solely on the provided data and information. The unclear methodology behind the LLM's diagnostic process leads to skepticism regarding the reliability of LLM-generated diagnoses. Consequently, their ability to accurately diagnose complex or unique cases may be limited, as demonstrated in the current study on skin histopathology cases. Notably, in a few cases, LLMs declined to provide a diagnosis on the initial prompt, citing concerns about giving medical advice, and only issued a diagnosis after repeated prompting with the same scenario. Despite their ability to offer insights based on existing knowledge, LLMs may lack a complete understanding of the intricate details and visual indicators crucial for pathologists' diagnosis. In the current study, the pathologist initially examined the histopathology slides and then provided the report to the AI systems. Another issue is that preserving the integrity of LLMs and safeguarding the confidentiality of associated data from unauthorized access is critical, particularly in scenarios involving sensitive patient information [14,15]. The case scenarios in this study did not include specific patient identifiers. Additionally, failure to evolve the LLM tools utilized in the pathological assessment alongside advancements in clinical practice and treatment poses the risk of stagnation and adherence to outdated methodologies. Although it is possible to manually update LLM algorithms to align with new protocols, their efficacy depends heavily on the availability of pertinent data, which might not be readily accessible during transitional periods. Such adaptations could introduce errors, particularly in pathology, through misclassifications of entities as classification and staging systems undergo revisions. Another concern is automation bias, which refers to the tendency of clinicians to regard LLM-based predictions as flawless or to adhere to them without questioning their validity. This bias often emerges soon after exposure to new technology and may stem from concerns about the legal consequences of disregarding an algorithm's output. Research across various fields has shown that automation bias can reduce clinician accuracy, affecting areas such as electrocardiogram interpretation and dermatologic diagnoses. Clinicians at all proficiency levels, including experts, are susceptible to this phenomenon [3,14-16].

The LLM has numerous applications in the medical field, with various technologies being developed at an unprecedented pace. For example, in the field of epilepsy, Empatica has created a wearable monitor called Embrace, which detects the onset of seizures in patients with epilepsy and notifies designated family members or trusted physicians. This innovation enhances safety and facilitates early management of such cases and received FDA approval six years ago [17]. Additionally, one of the earliest uses of LLM was for the detection of atrial fibrillation. AliveCor mobile application, which facilitates ECG monitoring and atrial fibrillation detection using a mobile phone, was FDA-approved. Recent findings from the REHEARSE-AF study indicated that traditional care methods are less effective at detecting atrial fibrillation in ambulatory individuals compared to remote ECG monitoring using Kardia [17,18]. Another example is the artificial immune recognition system, which has demonstrated remarkable accuracy in diagnosing tuberculosis by using support vector machine classifiers. These advanced systems significantly outperform traditional methods, making them a robust tool in identifying tuberculosis cases with high reliability. This underscores the potential of these models to enhance diagnostic processes in infectious diseases [19]. The advancements across various medical disciplines render the application of LLMs in histopathological diagnostics increasingly viable and anticipated for future clinical implementation. This progress motivates further research by scientists and numerous companies, as the focus has shifted from questioning whether LLM will be used in pathology or not to when and how these models will be utilized precisely.

One limitation of this study is that the aforementioned LLM systems were not evaluated for their ability and accuracy in directly reaching a diagnosis from histopathological images. Instead, the study relied on providing necessary information from the histopathological reports in text form, which imposes practical constraints and still requires an expert pathologist. Future studies focusing on both histopathological images and texts are necessary to further evaluate the comprehensive capabilities of LLM tools in this domain.

Conclusion

In certain instances, ChatGPT-3.5 and Gemini may provide an accurate diagnosis of skin conditions when provided with pertinent patient history and descriptions of histopathological reports. Specifically, Gemini showed higher accuracy in diagnosing non-neoplastic cases, while ChatGPT-3.5 demonstrated better performance in neoplastic cases. However, despite these strengths, the overall performance of both models is insufficient for reliable use in real-life clinical settings.

Declarations

Conflicts of interest: The author(s) have no conflicts of interest to disclose.

Ethical approval: Not applicable.

Patient consent (participation and publication): Not applicable.

Funding: The present study received no financial support.

Acknowledgements: None to be declared.

Authors' contributions: RMA and AMA were significant contributors to the conception of the study and the literature search for related studies. DSH and SHM involved in the literature review, study design, and manuscript writing. TSA, HAY, RSA, and AMS were involved in the literature review, the study's design, the critical revision of the manuscript, and data collection. RMA and DSH confirm the authenticity of all the raw data. All authors approved the final version of the manuscript.

Use of AI: ChatGPT-3.5 was used to assist in language editing and improving the clarity of the introduction section. All content was reviewed and verified by the authors. Authors are fully responsible for the entire content of their manuscript.

Data availability statement: Not applicable.

Abstract

Introduction

The term “scientist” lacks a universally accepted definition, reflecting the evolving, interdisciplinary nature of scientific work and posing challenges for recognition, communication, and policy. This study aims to develop consensus-based definitions of the term “scientist” by engaging experienced scholars across diverse fields.

Methods

This study involved 156 scholars, each with at least 1,000 citations, recruited via convenience sampling. Fourteen scientist definitions, derived from literature and expert input, were assessed using a nine-point Likert scale via a structured google forms survey. The sample size was calculated using G*power (effect size = 0.5, power = 0.95), requiring at least 80 participants. Content Validity Index (CVI) was used for analysis. Definitions scoring ≥0.78 were accepted and included for final analysis, 0.70–0.78 were revised and re-evaluated, and <0.70 were excluded. Participation was voluntary and anonymous, ensuring ethical compliance and confidentiality.

Results

Of the 14 proposed definitions, six (42.9%) were excluded (CVI < 0.70), seven (50.0%) were accepted (CVI > 0.78), and one (7.1%) underwent revision (CVI 0.70–0.78). The highest-rated definitions were refined into two consensus-based versions: a short definition (“A scientist is a person who conducts research”) and a detailed one emphasizing hypothesis formulation and knowledge dissemination. Final validation yielded CVIs of 0.82 and 0.84, respectively, confirming strong expert agreement on both definitions.

Conclusion

This study developed two validated definitions of “scientist” emphasizing systematic research and knowledge dissemination. These definitions clarify the concept of scientific identity, providing a flexible yet rigorous framework applicable across academic, interdisciplinary, and policy-making contexts.

Introduction

The term "scientist" has undergone significant transformation since its inception, reflecting the dynamic nature of scientific inquiry and the evolving landscape of knowledge. This lack of clarity stems from the diverse roles and contributions of individuals in scientific fields, the evolving nature of research, and the interdisciplinary scope of modern science. Historically, figures such as Galileo and Newton were regarded as natural philosophers, a reflection of an earlier framework for knowledge production that has evolved alongside modern scientific advancements. Before twentieth century, the term "scientist" was commonly referred to as a "man of science," "natural philosopher," or by various other designations [1,2].

In contemporary contexts, scientists operate across a broad spectrum of fields, including medicine, biology, chemistry, physics, and social sciences, each employing methodologies tailored to their specific inquiries. For instance, biologists may design experiments to test hypotheses about living organisms, while social scientists might use qualitative methods to explore human behavior [3]. The Science Council defines a scientist as an individual who methodically collects and applies research and evidence to develop hypotheses, performs experiments, and shares results to advance knowledge in their field [4]. While National Cancer Institute defines a scientist as an individual with a background in science, particularly someone actively engaged in a specific area of research [5]. This diversity in practices underscores the challenge of defining "scientist" in a way that captures the breadth of their contributions.

The plurality of definitions extends to global organizations and frameworks. For example, the United Nations Educational, Scientific, and Cultural Organization highlights the critical role of scientists in addressing global challenges and promoting sustainable development. This definition broadens the scope to include individuals working in multidisciplinary teams or applying scientific knowledge to public policy and societal issues. Similarly, some academic discussions focus on the characteristics of a scientist, such as curiosity, skepticism, and a commitment to evidence-based conclusions, rather than formal qualifications or job titles [6].

Unlike well-defined professions such as medicine or engineering, where specific educational pathways and professional titles (e.g., "doctor" or "engineer") confer clear identities, the term "scientist" lacks a universally recognized credentialing system. This absence can lead to underrepresentation or misrepresentation of scientific expertise, especially in interdisciplinary and collaborative contexts [7]. For example, the growing integration of data science in biology or physics illustrates the importance of understanding who qualifies as a scientist to ensure effective communication and collaboration among stakeholders. The absence of a standardized definition poses practical challenges for scientific communication, policymaking, and inclusivity. This study aims to address this gap by engaging scholars across disciplines to develop a consensus-based definition of "scientist." By recognizing the diverse and interdisciplinary contributions of scientists, such a definition could enhance collaboration, improve public understanding, and inform policies that support the scientific community.

Methods

Study design and participants

A total of 156 scholars (out of 300 invited) participated in this study. Eligibility was determined based on the scholars' substantial academic expertise, evidenced by the achievement of at least 1,000 citations within their respective fields. This criterion ensured that participants had significant research experience and were highly qualified to contribute to the formulation of a consensus-based definition of "scientist." Participants were recruited through a convenience sampling method, and data were collected via a structured survey administered through google forms. While convenience sampling was used due to the accessibility of high-citation scholars, efforts were made to ensure disciplinary diversity to mitigate potential bias. Personalized invitations were sent via email to each scholar to facilitate their inclusion in the study.

Sample size determination

The sample size was determined using G*power statistical software (version 3.1.9.7), employing a two-tailed goodness of fit test with an effect size of 0.5, an alpha error probability of 0.05, and a statistical power of 0.95. According to the calculations, a minimum of 80 participants were required to achieve statistically valid results. Consequently, 156 scholars were recruited to participate in the study, ensuring robust representation and adequate statistical power.

Data collection

Fourteen proposed definitions of "scientist," curated from existing literature and expert contributions, were presented to the enrolled scholars for evaluation (Table 1). Each definition included a Likert scale with nine response options, ranging from "strongly agree" to "strongly disagree." Responses were systematically recorded and compiled in an Excel sheet for subsequent analysis. This process facilitated the systematic capture of scholarly consensus on each definition.

Data analysis

The Content Validity Index (CVI) was employed to assess the relevance and agreement of the definitions. Definitions with a CVI below 0.70 were excluded, as they failed to meet the minimum threshold for consensus. Definitions with a CVI between 0.70 and 0.78 underwent a second round of evaluation, with refined wording sent back to the same scholars for further review. Definitions achieving a CVI above 0.78 were deemed sufficiently valid for inclusion in the final analysis [8]. These definitions formed the foundation for the development of a unified, consensus-based definition of "scientist."

Ethical considerations

Participation in the study was entirely voluntary, and all responses were anonymized to preserve participant confidentiality.

Results

Initially, out of the 14 proposed definitions of the term "scientist," six (42.9%) received a CVI score below the threshold of 0.70 and were consequently excluded from further consideration. In contrast, seven definitions (50.0%) demonstrated strong content validity with CVI scores equal to or exceeding 0.78 and were therefore retained for subsequent synthesis and analysis. Only one definition (7.1%) fell within the intermediate range, with a CVI between 0.70 and 0.78 (Table 2).

Through a rigorous, iterative evaluation process involving expert feedback, the definitions with the highest CVI scores (those above 0.78) were integrated and refined into two distinct, consensus-based definitions of the term "scientist." The first was a concise definition: “A scientist is a person who conducts research.” The second was a more comprehensive and elaborated definition: “A scientist is someone who systematically conducts or gathers and uses research to formulate hypotheses and test them, in order to gain and disseminate understanding and knowledge.”

These two final definitions were subsequently circulated among the panel of scholars for a second round of evaluation, during which they were asked to rate the definitions for content validity. The short definition received a CVI of 0.82, while the more detailed definition attained a slightly higher CVI of 0.84, reflecting strong agreement among the experts. Although no additional formal qualitative feedback was solicited at this stage; minor wording adjustments were made based on informal suggestions received during this validation round.

Discussion

The role of a scientist extends far beyond the stereotypical image of an individual in a white coat working exclusively in a laboratory setting. Careers grounded in scientific expertise are remarkably diverse, encompassing domains such as research, education, industry, and regulatory affairs. The Science Council categorizes scientists into 10 different types, highlighting the diversity of scientific roles beyond the stereotypical lab-based researcher. It includes types such as experimental scientists, theoretical scientists, data scientists, and more, reflecting the broad spectrum of scientific work today [9]. Definitions of the term “scientist” vary, yet they generally converge on the principles of systematic inquiry, evidence-based investigation, and the pursuit of knowledge across various disciplines. For instance, the Oxford Advanced Learner’s Dictionary and the Britannica Dictionary emphasize formal training and research functions, typically within the natural sciences such as biology, chemistry, or physics [10,11]. In contrast, contemporary perspectives, such as those discussed by the American Association for the Advancement of Science in 2024, recognize a broader spectrum of scientific engagement, encompassing both professional researchers and individuals committed to understanding the world through observation, experimentation, and analysis [12]. In light of this diversity, the present study aimed to clarify and formalize the definition of a "scientist" through expert consensus. Two definitions were developed: a concise definition “A scientist is a person who conducts research”, and a comprehensive definition “A scientist is someone who systematically conducts or gathers and uses research to formulate hypotheses and test them, in order to gain and disseminate understanding and knowledge.” These definitions encapsulate the core activities and guiding principles of scientific inquiry, emphasizing both methodological rigor and the essential role of knowledge dissemination across disciplines.

A key finding of this study lies in its recognition of the evolving tension between disciplinary specialization and the increasing importance of interdisciplinary collaboration. As highlighted in contemporary analyses of interdisciplinary research and development, scientists now frequently operate at the intersection of multiple fields, such as nanomedicine, where the diversity and dissimilarity of collaborators’ knowledge can significantly enhance research productivity [13]. The concise definition, "A scientist is a person who conducts research" captures this shift by avoiding constraints tied to specific disciplinary boundaries. In contrast, the more detailed definition explicitly incorporates the systematic formulation and testing of hypotheses, along with the dissemination of knowledge, thereby reinforcing the structured and communicative nature of scientific inquiry. These elements align closely with UNESCO’s 2019 call for stronger science-society engagement and underscore the ethical responsibilities inherent in modern scientific practice [14].

The study’s findings also contribute to ongoing debates surrounding professional identity within the scientific community. In contrast to regulated professions such as medicine, the absence of a universal credentialing system for scientists complicates formal recognition, particularly in non-academic and interdisciplinary contexts. This ambiguity is reflected in the National Cancer Institute’s pragmatic definition of a scientist, which emphasizes active participation in research rather than reliance on formal titles or qualifications [15]. By anchoring the term “scientist” in core research activities rather than occupational labels, the consensus-based definitions proposed in this study offer a more inclusive framework. This approach accommodates emerging roles in fields such as data science and applied research, thereby addressing the risk of under recognition in collaborative and cross-sector environments.

The dual definitions, concise and comprehensive, offer flexibility for different contexts, a strategy aligned with the Science Council’s emphasis on methodological diversity [4]. The detailed definition’s focus on systematic inquiry and dissemination aligns with studies of interdisciplinary science, where “impassioned commitment” to shared goals drives innovation [13]. Simultaneously, the availability of a concise definition enhances clarity in public discourse and science communication, while the more detailed version provides the specificity necessary for institutional contexts such as policy development, research funding, and professional accreditation.

Notably, the study’s findings also challenge enduring stereotypes of the “lone genius” scientist by highlighting the inherently collaborative and iterative nature of scientific practice. Contemporary frameworks, such as those emerging from computational biology, suggest that scientific identity is increasingly dynamic, pluralistic, and shaped by collective knowledge production [16]. The process undertaken in this study, involving successive refinement and expert validation of definitions, closely mirrors the recursive logic of the scientific method itself. This methodological alignment is particularly salient in fields like nutritional epidemiology, where the replication of findings remains a persistent challenge and iterative inquiry is essential for refining evidence [17].

Despite the methodological rigor and expert involvement, several limitations should be acknowledged. First, the study employed convenience sampling, which may introduce selection bias and limit the generalizability of the findings. Although participants were selected based on a minimum citation threshold to ensure scholarly expertise, this criterion may have inadvertently excluded emerging researchers or experts with significant practical contributions who have not yet achieved high citation metrics. Second, the use of an online survey format may have constrained participant engagement, as scholars with limited availability or preference for alternative formats may have been underrepresented. Additionally, response bias cannot be ruled out, as those with a particular interest in the topic or in defining scientific identity may have been more inclined to participate, potentially skewing the results. Future refinements of the definition should also consider voices from non-academic scientific contexts including those in industry, policy, and community-based science who are increasingly central to addressing complex global challenges.

Conclusion

By engaging experienced scholars across disciplines, this study establishes two validated definitions of “scientist” that emphasize systematic research activity and knowledge dissemination. These definitions offer a structured yet adaptable framework for understanding scientific identity, balancing clarity with flexibility. They help address the ambiguity surrounding the term “scientist,” providing a foundation for improved communication, interdisciplinary collaboration, and evidence-informed policy development. Importantly, they remain open to future refinement as scientific practice continues to evolve.

Declarations

Conflicts of interest: The authors have no conflicts of interest to disclose.

Ethical approval: Not applicable.

Patient consent (participation and publication): Not applicable.

Funding: The present study received no financial support.

Acknowledgements: None to be declared.

Authors' contributions: JG, MM, SB, BS, VS, ASN, SHM, HAH, AGH, ADS, RAK, WRR, AB, GB, SS, SN, CJ, PL, MSS, ZK, MC, AM, SK, FCT, FB, FRK, MAM, AA, VK, DH, PM, VRM, MSA, EA, and RV were significant contributors to the conception of the study, voting for the items. FHK, BAA, and AMM were involved in the literature review, manuscript writing, and data analysis and interpretation.  FHK and AMM Confirmation of the authenticity of all the raw data. All authors have read and approved the final version of the manuscript.

Use of AI: ChatGPT-3.5 was used to assist in language editing and improving the clarity of the manuscript. All content was reviewed and verified by the authors. Authors are fully responsible for the entire content of their manuscript.

Data availability statement: Not applicable.

Abstract

Introduction

Previously, the conventional surgical procedure of high-ligation and saphenous stripping was commonly used to treat varicose veins (VVs). However, contemporary advancements have led to the rapid evolution of VV management. This study shares a single center's experience in treating patients with lower limb VVs through endovenous laser ablation in combination with phlebectomy and sclerotherapy using multimodal analgesia.

Methods

This case series study included consecutive patients diagnosed with lower limb VVs. The inclusion criteria encompassed VVs categorized from score C1 to C6 clinical, etiologic, anatomic, and pathophysiological (CEAP), saphenofemoral incompetence, and patients aged between 18 and 75.

Results

A total of 153 patients were enrolled. The majority were female (73.0%), resulting in a female-to-male ratio of 2.73:1. The age of patients ranged from 18 to 73 years, with a mean age of 40.8 ± 11.7 years. Regarding post-procedural complications, wounds developed in 25 patients (16.3%), making it the most common complication, while thrombophlebitis occurred in 15 cases (9.8%), skin discoloration in nine cases (5.9%), and recanalization and DVT each in a case (0.7%). Due to extensive varicose veins, 31 patients (20.0%) required a sclerotherapy session six weeks after the operation. Patients could return to routine daily activities within 4 to 10 hours. Overall, the patient satisfaction rate (complete and partial) was 85%. Only a case of recurrence (0.7%) was reported after a one-year follow-up.

Conclusion

Endovenous laser ablation, in combination with phlebectomy and sclerotherapy using multimodal analgesia, may yield a satisfactory outcome in patients with moderate to severe VVs.

Introduction

Varicose veins (VVs) are abnormally twisted and dilated blood vessels, typically located in the lower limbs. They originate from damaged or faulty venous valves, which may subsequently give rise to painful swelling and the potential formation of blood clots [1]. It is the most commonly encountered among vascular diseases, affecting up to one-third of the population and profoundly impacting the quality of life [2]. Increased age, female gender, multiparity, obesity, a history of deep venous thrombosis (DVT), and engagement in occupations involving extended periods of standing are all recognized as significant risk factors associated with the onset of VVs [3]. Historically, VVs were often viewed as a cosmetic problem, and patient preferences largely influenced treatment decisions. However, advancements in medical imaging, particularly duplex ultrasonography, revolutionized the understanding of VVs by providing a more precise assessment of venous reflux and allowing healthcare professionals to accurately diagnose the underlying venous insufficiency contributing to VVs [4]. Previously, the conventional surgical procedure of high-ligation and saphenous stripping was commonly used for the treatment of VVs. However, contemporary advancements have led to the rapid evolution of VV management, offering minimally invasive interventions such as radiofrequency (RFA), laser ablations, foam, liquid sclerotherapy, and microphlebectomy [5]. Endovenous laser ablation (EVLA) uses laser energy to occlude impaired veins, rerouting blood circulation toward healthier venous pathways. Microphlebectomy presents another alternative therapeutic modality involving the surgical removal of superficial veins via small puncture incisions. Sclerotherapy entails the injection of a sclerosing agent into afflicted veins, resulting in their closure and redirection of blood flow [6]. This study aims to share the experience of a single center in treating patients with lower limb VVs through EVLA in combination with phlebectomy and sclerotherapy under multimodal analgesia. The study avoided citing suspicious data by checking for predatory behavior among the referenced studies [7].

Methods

Study design

The study was a single-center case series encompassing consecutive patients diagnosed with lower limb VVs and treated at the Thoracic and Vascular Surgery Department of Smart Health Tower from January 2020 to September 2023. Patients provided consent to participate in the study and to authorize the publication of any related data.

Data collection

Following data de-identification, the necessary information was retrospectively gathered from patients' profiles within the department's database. This included patient demographics, chief complaint, clinical score, affected side, severity of the condition, ultrasound examination results, and treatment outcomes.

Eligibility criteria

The inclusion criteria encompassed VVs categorized from score C1 to C6 (clinical, etiologic, anatomic, and pathophysiological) (CEAP), presence of saphenofemoral incompetence, and patients aged between 18 and 75 years. The exclusion criteria comprised any prior treatment of VVs, suspicion or confirmation of DVT or occlusion, contraindications to anesthesia, and refusal to undergo the treatment procedure.

Intervention

In the pre-operative phase, approximately 30 minutes before the operation, patients received a combination of medications, including pethidine (50 mg subcutaneously), ketorolac (30 mg intramuscularly), and ondansetron (8 mg), if there were no contraindications. In the operating theater, intravenous pethidine was administered after properly marking the varicose vein sites by injecting normal saline, patient positioning, and monitoring. This was followed by a slow tramadol infusion (100 mg) and paracetamol (1000 mg). Continuous administration of dexmedetomidine (100 –150 µg in 100 ml of normal saline) was initiated, with oxygen supplementation adjusted as necessary. Antiemetic drugs, aside from ondansetron, were administered during the procedure unless contraindicated. In rare cases of anxiety or the requirement for minimal sedation, fentanyl (50 µg), midazolam (1mg), or a combination of both were included. 

The EVLA procedure started with a Doppler ultrasound by a radiologist to guide a needle to access the great saphenous vein (GSV). Then, a guide wire was carefully inserted into the vein, followed by the placement of an introducer sheath, which might include a dilator. Just below knee level was the preferred entry point for the insufficient GSV due to its larger size, straight course, and lower risk of nerve injury. The laser (1470 nm) was inserted into the varicose vein after verifying the laser's tip position by observing the red standby light through the skin with a diameter between 200 and 600 mm (dependent on the varicose vein and the laser parameters). The laser's settings were customized as necessary, and it was carefully withdrawn through the vein using a technique suited to the specific situation. The laser power was calibrated according to the vein size, with a minimum setting of 42 watts. The power was determined by multiplying the vein diameter by a factor of 7. After major vein ablation, micro-avulsion was done for the visible varicose veins using a particular instrument (Drosh) (Figure 1). The reticular and telangiectasia were injected with a foamy solution consisting of two ml of polidocanol and eight ml of atmospheric air (1:4) as the sclerosing agent.

Statistical analysis

The data organization was conducted using Microsoft Excel 2019. Descriptive analysis was performed using the Statistical Package for the Social Sciences (SPSS) Version 25 for qualitative data synthesis. The data were presented as medians, means, standard deviations, frequencies, percentages, and ranges.

Results

A total of 153 patients were enrolled in this study. The majority were female (73.0%), resulting in a female-to-male ratio of 2.73:1. The age of patients ranged from 18 to 73 years, with a mean age of 40.8 ± 11.7 years. Over half of the cases were overweight (54.9%), and the mean BMI was 26.3 ± 4.2. The major chief complaint was pain (17.7%), followed by swelling (15.0%) and lower limb discoloration (5.2%). The disease was asymptomatic in 59.5% of the cases. The disease presented as VVs (C2) in most cases (73.9%), although 21 (13.7%) presented with reticular veins (C1) and 10 (6.5%) with telangiectasia (C1). Disease severity was moderate in over half of the cases (53.6%), followed by severe in 51 cases (33.3%). The majority of patients (61.4%) had bilateral lower limb involvement, while the remaining patients had unilateral involvement, with 22.2% affected in the left lower limb and 16.3% in the right lower limb. The mean diameters of the GSV undergoing treatment were 9 mm. 

The mean reflux duration of the affected veins was 1.1 seconds, ranging from 0.6 to 2.5 seconds, with the procedure duration ranging from 45 to 220 minutes. Regarding post-procedural complications, wounds developed at the site of sclerosant injection in 25 patients (16.3%), making it the most common complication, while thrombophlebitis occurred in 15 cases (9.8%), skin discoloration in nine cases (5.9%), and recanalization and DVT each in a case (0.7%). The thrombophlebitis cases were treated with an anti-inflammatory agent. Wounds healed with proper dressing within two to three weeks. Due to extensive VVs, 31 patients (20.0%) required sclerotherapy six weeks after the operation. Additionally, all instances of skin discoloration resolved spontaneously. Patients could return to routine daily activities within 4 to 10 hours. Overall, 54.3% of the patients were completely satisfied with the outcome. Meanwhile, 30.7% were partially satisfied, and 15.0% were not satisfied with the procedure. Only a case of recurrence was reported during the one-year follow-up (Table 1).

Discussion

The VVs represent a prevalent condition, with occurrence rates typically ranging from 29.5% to 39.0% in women and 10.4% to 23.0% in men [1]. Annually, the condition manifests in approximately 2.6% of women and 1.9% of men. Its prevalence consistently rises with age, a trend often associated with increased height, weight, and body mass [1,8]. Chronic venous disease exacerbates the severity of symptoms, progressing from telangiectasia, characterized by the formation of threadlike patterns on the skin (referred to as spider veins), to VVs. This progression often imposes a substantial negative impact on the patient's quality of life [9]. In the current study, consistent with the genuine literature, females were predominantly affected, with a female-to-male ratio of 2.73:1. The mean age of patients, 40.8 years, aligns with previous findings [1,8]. The primary manifestation of the disease was VVs in the majority of cases (73.9%), indicating significant disease progression. Among the cases, the severity was moderate in 53.6% and severe in 33.3%.

Aligned with technological advancements, continual investigation has been undertaken to explore therapeutic approaches for treating VVs through endovenous methodologies [10]. A pivotal milestone occurred in 2001 when Navarro et al. reported the inaugural utilization of thermal endovenous ablation employing an 810 nm diode laser [11]. Subsequently, a consistent evolution in laser technology has ensued, accompanied by many studies employing diverse wavelengths and laser modalities [10,12,13]. At present, the Food and Drug Administration (FDA) has approved lasers of various wavelengths, including 810, 940, 980, and 1470 nm diode lasers, alongside 1319 and 1320 nm neodymium-doped yttrium aluminum garnet (Nd: YAG) lasers. Simultaneously, the progression of laser technology has stimulated research into thermal ablation techniques for the GSV using radiofrequency energy. The FDA endorsement for the application of radiofrequency energy in endovenous ablation procedures was granted in 1999 [10]. In 2002, Weiss et al. published the initial cases wherein patients underwent thermal ablation employing radiofrequency energy [14]. The current clinical practice guidelines advocate for ablation (EVLA and RFA) as the primary treatment for superficial venous insufficiency while recommending phlebectomy or sclerotherapy for addressing varicosities [15]. In the present study, EVLA (1470 nm) was employed in combination with phlebectomy and sclerotherapy to manage VVs in 153 patients.

A meta-analysis of 119 studies found success rates of 94% for EVLA and 84% for RFA from data on 12,320 legs [16]. Puggioni et al. reported one-month follow-up success rates of 100% for EVLA and 96% for RFA [17]. Bozoglan et al. reported no significant complications such as DVT, pulmonary embolism, or skin burns in either EVLA or RFA. Minor complications included induration (20.7% in EVLA and 31% in RFA), ecchymosis (31% in EVLA and 27.6% in RFA), and edema (27.6% in EVLA and 65.5% in RFA). They claimed that most minor complications, such as hematoma and ecchymosis, were attributed to the use of tumescent anesthesia rather than the procedures themselves. They reported a recanalization rate of 6.8% in the RFA group, while no instances of recanalization were noted in the EVLA group. Patient satisfaction levels were significantly higher with EVLA at 51.7% compared to the 31.0% satisfaction rate observed for RFA. Only 17.2% of patients reported satisfaction with both procedures. Individuals in the EVLA group typically returned to daily activities within 0.9 days, while those in the RFA group took an average of 1.3 days to resume regular activities. The mean duration of procedures was 31.2 minutes for EVLA and 32.7 minutes for RFA [10]. Another study involving 148 patients undergoing EVLA reported pain in over 50% of patients, hematoma in 40.5%, superficial vein thrombosis in 6.8%, induration in 6.8%, hyperpigmentation in 3.4%, infection and dysesthesia in less than 2%, and nerve damage occurring in less than 1% of cases [18]. Additionally, a meta-analysis reported the incidence of thrombophlebitis and hematoma at 4.9% and 4.4%, respectively [19]. Kawai et al. performed EVLA with and without phlebectomy and found hematoma in 26.6% of cases with phlebectomy and 23.2% of cases without phlebectomy. They reported no instances of DVT [15]. Because sclerotherapy plays a significant role in managing and diminishing the appearance of VVs, which have reached a severe stage [1], they needed additional sclerotherapy in 8 cases (3.2%) in the phlebectomy group and 26 cases (3.7%) in the non-phlebectomy group. The operation time ranged from 27 to 40 minutes in the EVLA with the phlebectomy group and 19 to 31 minutes in the counterpart group. Recanalization was observed in four cases (1.6%) in the EVLA with the phlebectomy group and in 25 cases (3.6%) in the latter group [15]. In this study, developing wounds was the most prevalent complication (16.3%), followed by thrombophlebitis (9.8%) and skin discoloration (5.9%). Thrombophlebitis was treated with an anti-inflammatory agent, skin discoloration resolved spontaneously, and wounds healed within a few weeks with dressing. The duration of the operation in the present study ranged from 45 to 220 minutes, with a mean of 70 minutes. The recanalization rate (0.7%) was lower than the rate reported by Kawai et al. [15] but higher than that reported by Bozoglan et al [10]. Patients returned to daily activities within 4 to 10 hours, and the overall treatment satisfaction (partial and complete) reached 85%, surpassing Bozoglan et al.'s [10]. There was a case of DVT, and sclerotherapy was required in 20% of cases due to the severity of the disease.

Regarding the working principle, EVLA enables veins to absorb energy for heat generation, leading to vein obliteration. The energy utilized during the procedure significantly impacts the outcome. It has been discovered that energy delivery directly influences recurrence, with lower energy levels yielding poorer outcomes than higher energy levels [20]. The recurrence rate for up to 10 years was found to be 66% in EVLA, and 36% of the patients needed reintervention [21]. To mitigate the risk of recurrence, a laser wavelength of 1470 nm was employed in the procedures of the current study. After one year of follow-up, only a case of recurrence was reported. This study has some drawbacks, including a retrospective design that means crucial data may be overlooked for discussion and a short follow-up period to properly indicate the recurrence rate.

Conclusion

Endovenous laser ablation, in combination with phlebectomy and sclerotherapy using multimodal analgesia, may yield a satisfactory outcome in patients with moderate to severe VVs.

Declarations

Conflicts of interest: The authors have no conflicts of interest to disclose.

Ethical approval: Not applicable.

Patient consent (participation and publication): Patients provided consent to participate in the study and to authorize the publication of any related data.

Source of Funding: Smart Health Tower.

Role of Funder: The funder remained independent, refraining from   involvement   in   data   collection, analysis, or   result formulation, ensuring unbiased research free from external influence.

Acknowledgements: None to be declared.

Authors' contributions: FHK was a major contributor to the study's conception and to the literature search for related studies. SHT, AAM, and RJR were the radiologists who provided access to the cases. HOA, BAA, and MNH were involved in the literature review, study design, and writing of the manuscript. FEF, KAS, and BYA were the anesthesiologists who anaesthetized the cases. DHMS, FJA, NSS, PMK, BAA, AMM and SJJ were involved in the literature review, the study's design, the critical revision of the manuscript, and the table processing. All authors have read and approved the final version of the manuscript.

Use of AI: ChatGPT-3.5 was used to assist in language editing and improving the clarity of the manuscript. All content was reviewed and verified by the authors. Authors are fully responsible for the entire content of their manuscript.

Data availability statement: Not applicable.