Evaluation of ChatGPT’s performance on answering pediatric urology questions based on association guidelines
Bibliographic record
Abstract
INTRODUCTION: ChatGPT has been shown to provide accurate and complete responses to clinically focused questions, although its ability to successfully answer common pediatric urology-based questions remains unexplored. Furthermore, the concordance of ChatGPT's answers with association recommendations has yet to be analyzed. METHODS: A list of common pediatric urology questions of varying difficulty was developed in association with publicly available guidelines and resources from the Canadian Urological Association (CUA), American Urological Association (AUA), and the European Association of Urology (EAU). Questions were administered individually using three separate functions, and responses were evaluated for comprehensiveness and accuracy using a Likert scale. Descriptive statistics and analysis of variance were used for statistical analysis. RESULTS: ChatGPT performed best in the domain of phimosis (mean ± standard deviation: 2.32/3.00±0.57) and VUR (2.11/3.00±0.63), and worst in acute scrotal pathology (1.90/3.00±0.58) and cryptorchidism (1.92/3.00±0.56) (p=0.031). "Easy" questions (2.31/3.00±0.09) had greater comprehensiveness scores compared to "medium" (1.92/3.00±0.07, p=0.003) and "difficult" questions (1.86/3.00±0.101, p=0.003). Definition-based questions had greater comprehensiveness scores across all guidelines. ChatGPT was more accurate and in concordance with EAU-based information (2.10±0.41) compared to AUA (1.95±0.41, p=0.04). CONCLUSIONS: ChatGPT answered questions with high levels of appropriateness and comprehensiveness. ChatGPT performed best in the areas of phimosis and VUR and worst in acute scrotal pathology. While ChatGPT performed well across all question domains, it performed best when referenced to EAU and CUA compared to AUA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".