Can ChatGPT Replace an Otolaryngologist in Guiding Parents on Tonsillectomy?
Bibliographic record
Abstract
Background: ChatGPT is an artificial intelligence tool, which utilizes machine learning to analyze and generate human-like text. The user-friendly accessibility of this tool enables patients conveniently access medical information without intricate terminology challenges. The objective of this study was to assess the accuracy of ChatGPT in providing insights into indications and management of complications after tonsillectomy, a common pediatric otolaryngology procedure. Methods: The responses generated by ChatGPT were compared to the “Clinical practice guidelines: tonsillectomy in children—executive summary” developed by the American Academy of Otolaryngology—Head and Neck Surgery Foundation (AAO-HNSF). An assessment was carried out by presenting predetermined questions regarding indications and complications post tonsillectomy to ChatGPT, followed by a comparison of its responses with the established guideline by 2 otolaryngology experts. The responses of both parties were reviewed by the senior author. Results: A total of 16 responses generated by ChatGPT were assessed. After a comprehensive review, it was concluded that 15 out of 16 (93.8%) responses demonstrated a high degree of reliability and accuracy, closely adhering to the standard established by the AAO-HNSF guideline. Conclusion: The results validate the potential of using ChatGPT to enhance healthcare delivery making guidelines more accessible to patients while also emphasizing the importance of ensuring the provision of accurate and reliable medical advice to patients.
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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 | 0.000 |
| 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 | 0.001 |
| 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".