The Implementation of International Electives for Plastic Surgery Residents: Current State and Future Directions
Bibliographic record
Abstract
INTRODUCTION: Interest in international surgical missions has been rising exponentially, with the plastic surgery community being a leader in this endeavor. The role of residents in such missions remains a topic of debate. This systematic review aims to consolidate the literature relevant to the inclusion of plastic surgery residents on international surgical missions to devise an algorithm to facilitate resident participation. MATERIALS AND METHODS: A comprehensive search of PubMed, Medline, and EMBASE was performed to identify studies relevant to plastic surgery resident involvement in the context of surgical missions. Relevant conclusions were retrieved from each study and compiled according to category. RESULTS: Of 418 initial studies, 26 were retained for the qualitative synthesis. These were grouped into 3 categories: surveys (n=12), reflections (n=7), and reviews (n=7). The survey studies addressed the perceived value, educational impact, and long-term effect on participating residents. Three reflection studies were from the perspective of residents and 4 from staff, while all recounted the many benefits gained for participating residents. Review studies addressed the issue of accreditation and the ethics surrounding resident involvement. CONCLUSION: This systematic review highlights the overwhelming support from residents and staff, the highly regarded educational value, and the positive global health effects associated with plastic surgery resident participation in international surgical missions. The authors hope this will encourage and facilitate the implementation of formal opportunities for residents within residency 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 | 0.034 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".