Comparative Performance of the Leading Large Language Models in Answering Complex Rhinoplasty Consultation Questions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it