Variability in Hand Surgery Training Among Plastic and Orthopaedic Surgery Residents
Bibliographic record
Abstract
BACKGROUND: A career in hand surgery in the United States requires a 1-year fellowship after residency training. Different residency specialty programs may vary in case volume. The purpose of this study was to characterize variation in hand surgery training within and between orthopaedic and plastic surgery residents. METHODS: Publicly available hand surgery case logs for graduating orthopaedic and plastic surgery residents during the 2010 to 2011 to 2018 to 2019 academic years were obtained through the Accreditation Council of Graduate Medical Education. Student t-tests were used to compare mean case volumes among several categories between plastic surgery (PRS) and orthopaedic surgery (OS) residents. Intraspecialty variation was assessed by comparing the 90th and 10th percentiles in each category. RESULTS: A total of 6,254 orthopaedic and 1,070 plastic surgery graduating residents were included. The mean hand surgery case volume for orthopaedic residents (OS 247.0) was significantly lower than that for plastic surgery residents (PRS 412.0) (P < 0.0001). Orthopaedic residents performed more trauma cases (OS 133.2, PRS 54.5; P < 0.0001) but fewer nerve repairs (OS 3.3, PRS 28.5 P < 0.0001) and amputations (OS 6.4, PRS 15.8; P < 0.0001). Nerve decompression case volumes were similar between the two specialties (OS 50.2, PRS 47.3; P = 0.34). Case volumes among orthopaedic residents varied considerably in amputations and among plastic surgery residents in replantation/revascularization procedures. CONCLUSIONS: Orthopaedic surgery residents performed significantly more trauma cases than plastic surgery residents did, but fewer overall cases, nerve repairs, and amputations, while nerve decompression volumes were similar between specialties. This information may help inform residency and fellowship directors regarding areas of potential training deficiency.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".