Exposure to plastic surgery during undergraduate medical training: A single-institution review
Bibliographic record
Abstract
Background Applications to surgical residency programs have declined over the past decade. Even highly competitive programs, such as plastic surgery, have begun to witness these effects. Studies have shown that early surgical exposure has a positive influence on career selection. Objective To review plastic surgery application trends across Canada, and to further investigate medical student exposure to plastic surgery. Methods To examine plastic surgery application trends, national data from the Canadian Resident Matching Service database were analyzed, comparing 2002 to 2007 with 2008 to 2013. To evaluate plastic surgery exposure, a survey of all undergraduate medical students at the University of Toronto (Toronto, Ontario) during the 2012/2013 academic year was conducted. Results Comparing 2002 to 2007 and 2008 to 2013, the average number of national plastic surgery training positions nearly doubled, while first-choice applicants decreased by 15.3%. The majority of Canadian academic institutions experienced a decrease in first-choice applicants; 84.7% of survey respondents indicated they had no exposure to plastic surgery during their medical education. Furthermore, 89.7% believed their education had not provided a basic understanding of issues commonly managed by plastic surgeons. The majority of students indicated they receive significantly less plastic surgery teaching than all other surgical subspecialties. More than 44% of students not considering plastic surgery as a career indicated they may be more likely to with increased exposure. Conclusion If there is a desire to grow the specialty through future generations, recruiting tactics to foster greater interest in plastic surgery must be altered. The present study suggests increased and earlier exposure for medical students is a potential solution.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".