The Montreal Plastic Surgery Residency Bootcamp: Structure and Utility
Bibliographic record
Abstract
Transitioning from medical school to surgical residency is a difficult endeavor. To facilitate this period, the University of Montreal's plastic surgery program developed and implemented an intensive 1-month bootcamp rotation. It is the only one of its kind and length amongst plastic surgery residency programs in North America. It includes didactic teachings in anatomy, cadaveric dissections, and surgical approaches for an array of procedures. Clinical and technical skills are reviewed with senior residents and attending surgeons. Research opportunities and case scenarios are also covered. An anonymous online 30-question survey was sent to all residents who participated in the bootcamp rotation between 2013 and 2020. Questions evaluated residents' knowledge of anatomy, basic surgical skills, common approaches, flap knowledge, and on-call case management, before and after the bootcamp. Seventeen plastic surgery residents responded to this questionnaire (81%). The majority confirmed that the bootcamp helped them prepare for residency, research, and on-calls, and also helped them expand their knowledge of anatomy and surgical skills. The residents responded positively to the bootcamp's structure and implementation. This study proposes that surgical programs could benefit from a bootcamp rotation at the beginning of their curriculum. The purpose is to facilitate the transition between medical school and postgraduate training, and to ensure a basic level of competence for all junior residents. Further prospective studies could demonstrate the bootcamp's impact in board certification rates and acceptance into fellowship training programs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".