The Impact of Subspecialty Fellows on Orthopaedic Resident Surgical Experience: A Multicenter Study of 51,111 Cases
Bibliographic record
Abstract
INTRODUCTION: Meaningful participation in surgery is important for orthopaedic resident education. This study aimed to quantify the effect of fellows on resident surgical experience. We hypothesized that as fellowship programs expanded, resident caseload would decrease, whereas "double-scrubbed" cases would increase. METHODS: This multicenter retrospective study included 9 years of surgical caselog data from two orthopaedic residency programs. Six subspecialty services on which fellow number varied over time were included (trauma, spine, foot and ankle, adult reconstruction, and hand). Case volume and personnel composition per case were extracted. Statistical analysis was performed with two-sample equal variance Student t-tests. RESULTS: A total of 51,111 cases were assessed. Surgical volume increased across all sites/services over time. Fellow numbers did not affect average resident caseload. However, in years with more fellows, an 11% decrease in one-on-one resident-attending cases (P = 0.002) and a 17% increase in resident-fellow-attending "double-scrubbed" cases was observed (P < 0.001). DISCUSSION: Increasing orthopaedic fellows did not affect resident case volume but resulted in fewer one-on-one cases with the attending and more "double-scrubbed" cases with a fellow. The implications of these findings to resident education require further study, but orthopaedic educators should be aware of these findings to try to maximize educational opportunities. LEVEL OF EVIDENCE: Level III.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".