Comparative Performance of the Leading Large Language Models in Answering Complex Rhinoplasty Consultation Questions
Bibliographic record
Abstract
Background: Various large language models (LLMs) can provide human-level medical discussions, but they have not been compared regarding rhinoplasty knowledge. Objective: To compare the leading LLMs in answering complex rhinoplasty consultation questions as evaluated by plastic surgeons. Methods: Ten open-ended rhinoplasty consultation questions were presented to ChatGPT-4o, Google Gemini, Claude, and Meta-AI LLMs. The responses were randomized and ranked by seven rhinoplasty-specializing plastic surgeons (1 = worst, 4 = best) considering their quality. Textual readability was analyzed via Flesch Reading Ease (FRE) and Flesch-Kincaid Grade (FKG). Results: Claude provided the top answers for seven questions while ChatGPT provided the top answers for three questions. In overall collective scoring, Claude provided the best answers with 224 points, followed by ChatGPT’s 200, Meta’s 138, and Gemini’s 138 scores. Claude (mean score/question 3.20 ± 1.00) significantly outperformed all the other models ( p < 0.05), while ChatGPT (mean score/question 2.86 ± 0.94) outperformed Meta and Gemini. Meta and Gemini performed similarly. Meta had a significantly lower FKG than Claude and ChatGPT and a significantly lower FRE than ChatGPT. Conclusion: According to ratings by seven rhinoplasty-specializing surgeons, Claude provided the best answers for a set of complex rhinoplasty consultation questions, followed by ChatGPT. Future studies are warranted to continue comparing these models as they evolve.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".