The Use of Large Language Models in Postgraduate Plastic Surgery Training: A National Survey of Plastic Surgery Residents
Bibliographic record
Abstract
Introduction: Large language models (LLMs) like ChatGPT are used by medical trainees and professionals for learning and clinical support. This study determined how Canadian plastic surgery residents utilize and perceive LLMs for their training. Methods: A cross-sectional survey was distributed to all Canadian, English-speaking plastic surgery trainees ( N = 100). Descriptive statistics and conventional content analysis were used to describe quantitative and free-text responses, respectively. Results: A total of n = 36 responses were collected (36% response rate) from Canadian plastic surgery residents. Among residents, 83.3% reported using LLMs for any purpose, and 63.8% reported using the technology for plastic surgery education. The most frequently utilized LLMs include ChatGPT (83.3%), BingAI (11.1%), and Gemini (8.3%). More than half of residents reported using LLMs a minimum of once per week (50.1%). The most common applications included explaining concepts (58.3%), explaining procedures (33.3%), answering lecture questions (27.8%), and creating presentations (27.8%). Of respondents, 94.4% reported not having received education or training on the use of LLMs, and 37.1% reported concerns with the use of the technology for plastic surgery learning. The themes that emerged from the free-text responses were categorized into 3 groups: (1) advantages, including time-efficiency and summarization, (2) disadvantages, including concerns of inaccuracies, confidentiality, and over-reliance, and (3) recommendations, such as didactic teaching sessions and workshops. Conclusions: LLMs are commonly used by Canadian plastic surgery residents for a variety of purposes. Most residents have not been trained on the optimal use of the technology, and surgical residency programs should consider formal LLM instruction to leverage the capabilities of this tool and mitigate potential harms.
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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 | 0.004 | 0.205 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 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".