Accuracy, Clarity, and Comprehensiveness of ChatGPT Outputs for Commonly Asked Questions About Living Kidney Donation
Bibliographic record
Abstract
INTRODUCTION: The effectiveness of ChatGPT responses to common living kidney donation (LKD) queries remains unclear. METHODS: We surveyed nephrologists and living kidney donors/candidates to evaluate ChatGPT-3.5's accuracy, comprehensiveness, and clarity in answering common donation questions in English and French. Ratings used a 5-point Likert scale, with percentage agreement and modified Fleiss' Kappa measuring inter-rater consistency. RESULTS: The evaluation of ChatGPT-3.5's responses varied between nephrologists and kidney donors/candidates. Nephrologists showed moderate percentage agreement for English responses (50%-59%) and poor agreement for French responses (9%-45%). Kidney donors/candidates exhibited high agreement for English (90%-100%) but low for French (0%-77%). Inter-rater agreement among nephrologists was moderate for both English (Kappa 0.74, 95% CI: 0.67, 0.79, p < 0.0001) and French (Kappa 0.70, 95% CI: 0.64, 0.77, p < 0.0001). In contrast, inter-rater agreement was poor among donors/candidates for both English (Kappa -0.10, 95% CI: -0.14, -0.07, p = 0.99) and French (Kappa -0.03, 95% CI: -0.07, 0, p = 0.81). CONCLUSION: ChatGPT 3.5's responses to common LKD queries demonstrated limited agreement among nephrologists and kidney donors/donor candidates, highlighting its lack of reliability as a supplement to existing educational materials for living kidney donor programs in English and French.
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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 | 0.062 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".