Assessing the utility of a natural language processing model in answering common urological questions
Bibliographic record
Abstract
Abstract Background ChatGPT, an interactive natural language processing model, is becoming increasingly used for medical information gathering. The objective of this study is to investigate the appropriateness of ChatGPT's responses to common urological questions compared to the Canadian Urological Association (CUA) guideline recommendations. Methods A list of 10 urological questions were developed from the CUA guidelines and patient information materials. Each question was asked three times in ChatGPT (Version 4), totaling 30 ChatGPT‐generated responses. Responses were assessed by three reviewers using a 4‐point Likert scale (0–3) to score appropriateness with CUA guidelines as a reference. The median values and variance of answer scores were calculated to form a consensus score to determine model reliability. Results Forty percent ( n = 12/30) of ChatGPT answers were deemed appropriate. The overall mean ± standard deviation score was 1.64 ± 0.85 (between “Some correct and some incorrect” and “Correct but inadequate”). When comparing question difficulty, “Easy” questions had an overall score of 1.87 ± 1.01, compared to “Medium” questions with a score of 1.31 ± 0.47. ChatGPT generated the most appropriate responses in the domains of prostate cancer (3.00 ± 0.25), erectile dysfunction (3.00 ± 0.28), andrology (3.00 ± 0.78), and kidney cancer (3.00 ± 1.00). The average variance between responses was 0.27, demonstrating strong model reliability. Conclusion This study examined the utility of ChatGPT in answering questions based on CUA clinical guidelines. Although promising, ChatGPT underperforms in answering common urological questions despite showing high levels of repeatability.
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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 | 0.033 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".