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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".