Evaluating the accuracy and reproducibility of ChatGPT responses in the context of cochlear implantation
Bibliographic record
Abstract
Background The use of ChatGPT in the field of otolaryngology is increasing; however, there are not enough studies related to cochlear implants.Aim/Objectives To assess the accuracy and reproducibility of ChatGPT (GPT-4o) responses to questions about cochlear implantation, evaluating its potential role in patient education.Material and Methods A total of 104 questions across five categories (basic, preoperative, surgical, postoperative care, postoperative expectations) were selected from reliable online sources. Each was posed twice to ChatGPT-4o in separate sessions. Responses were graded by two reviewers for accuracy (comprehensive/correct, partially correct, misleading, or incorrect/irrelevant). Reproducibility was assessed based on consistency across sessions. Discrepancies were resolved by a third expert reviewer.Results Of 104 responses, 84.6% were correct, 5.8% partially correct, 6.7% misleading, and 2.9% incorrect. Reproducibility was 88.4% overall and 100% in the surgery category. No significant differences were found between question categories for accuracy (p = 0.829) or reproducibility (p = 0.348).Conclusion and Significance ChatGPT provided highly accurate and reproducible responses to cochlear implant-related questions, supporting its use as an educational tool. Nonetheless, expert review remains essential for complex or critical topics.
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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 | 0.005 | 0.021 |
| 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".