How Competitive Is Plastic Surgery? An Analysis of the Canadian and American Residency Match
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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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.024 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".