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 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 | 0.078 | 0.290 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".