How Competitive Is Plastic Surgery? An Analysis of the Canadian and American Residency Match
Why this work is in the frame
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Bibliographic record
Abstract
Background: Plastic surgery (PS) is considered to be one of the most competitive specialties. As a result, some students are discouraged from applying, reducing the overall number of PS candidates. Still, much of what we know of the match is based in conjecture. Objective: To examine the Canadian PS match data from 1997 to 2016. To our knowledge, this is the first long-term analysis of the Canadian PS residency match. Method: We examined the Canadian Residency Matching Service reports from 1997 to 2016, extracting key match statistics, including available positions, number of applicants, positions filled, positions unfilled, and gender-specific match results. To examine competitiveness, the ratio of total applicants per quota per year (CR) and the ratio of applicants who chose PS as their first-choice specialty per quota per year were calculated (FC-CR). The National Residency Matching Program data were used to assess the American integrated PS match over the past decade and served as a comparison. Results: The CR of Canadian PS programs declined over the last 20 years ( P < .001), indicating fewer applicants applied to the program per available position. Similarly, the FC-CR also declined over the last 20 years ( P < .001). The number of females matching to their first-choice discipline of PS increased from 1997 ( P < .001). There was no significant change in the number of males matching to their first-choice discipline of PS ( P = .15). There was no significant change in the competitiveness (CR) of the American integrated PS match over the last decade ( P = 0.087). Conclusion: Encouragingly, today PS has more training positions and more female residents; yet, the overall number of applicants has remained relatively static over the past 20 years. This analysis serves as a valuable reference for PS programs and should assist in developing strategies to encourage the best applicants to apply.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| 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