Can a Machine Ace the Test? Assessing GPT-4.0’s Precision in Plastic Surgery Board Examinations
Bibliographic record
Abstract
Background: As artificial intelligence makes rapid inroads across various fields, its value in medical education is becoming increasingly evident. This study evaluates the performance of the GPT-4.0 large language model in responding to plastic surgery board examination questions and explores its potential as a learning tool. Methods: We used a selection of 50 questions from 19 different chapters of a widely-used plastic surgery reference. Responses generated by the GPT-4.0 model were assessed based on four parameters: accuracy, clarity, completeness, and conciseness. Correlation analyses were conducted to ascertain the relationship between these parameters and the overall performance of the model. Results: < 0.0001), whereas no significant correlation was found between accuracy and clarity or conciseness. Performance variability across different chapters indicates potential limitations of the model in dealing with certain complex topics in plastic surgery. Conclusions: The GPT-4.0 model exhibits considerable potential as an auxiliary tool for preparation for plastic surgery board examinations. Despite a few identified limitations, the generally high scores on key parameters suggest the model's ability to provide responses that are accurate, clear, complete, and concise. Future research should focus on enhancing the performance of artificial intelligence models in complex medical topics, further improving their applicability in medical education.
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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 | 0.019 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".