Analysis of Strengths in Exposure to Cases During Plastic and Orthopaedic Hand Surgery Fellowships
Bibliographic record
Abstract
INTRODUCTION: Prospective residents interested in hand surgery must decide to apply for hand surgery fellowships sponsored by different specialties. This study compares case volumes reported during plastic surgery and orthopaedic hand surgery fellowships. METHODS: The American Council for Graduate Medical Education case logs of accredited hand surgery fellowships were analyzed for hand surgery cases (2012 to 2013 to 2020 to 2021). The reported case volume was compared by specialty. Temporal trends were described, intrapathway variabilities calculated, and interpathway differences calculated with Student t -tests. RESULTS: Two hundred plastic surgery (13%) and 1,323 orthopaedic (87%) hand surgery fellows were included. The number of orthopaedic hand surgery fellowships increased from 58 in 2012 to 2013 to 70 in 2020 to 2021 (21% increase), whereas the number of plastic surgery fellowships was stable at 16. Orthopaedic hand surgery fellows reported more hand surgery cases (764 ± 22 versus 628 ± 226), arthroscopy cases (53 ± 54 versus 23 ± 38), and miscellaneous hand surgery cases (42 ± 23 versus 31 ± 18) than plastic surgery hand fellows. Plastic surgery hand fellows reported more cases in wound closure with graft, wound reconstruction with flap, nerve injury, and vascular repair. Overall, orthopaedic surgery offered more experience in 15 case categories (58%), while plastic surgery offered more experience in five case categories (19%). Six case categories (23%) had no difference between specialties. DISCUSSION: Although orthopaedic hand surgery fellowship affords more cases overall, plastic surgery hand fellowships have unique strengths in wound reconstruction with grafts and flaps, nerve injury, and vascular repair. Ultimately, results from this study create a benchmark to improve future training opportunities for hand surgery fellows and orthopaedic surgery residents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".