Orthopaedic Surgery Residency Match After an Early-Exposure Research Program for Medical Students
Bibliographic record
Abstract
INTRODUCTION: The purpose of this study was to determine the proportion of students matching in orthopaedic surgery after a structured, early-exposure mentored research program and what factors were associated with those students compared with participants who matched in other specialties. METHODS: Program data were reviewed from 2007 to 2015. Multivariable binary logistic regression analysis was used to evaluate student and research factors associated with orthopaedic surgery match. RESULTS: Of 174 students, 117 (67%) matched into surgical residency programs, with 49% (n = 85) matching into orthopaedic surgery. The percentage of women matching into orthopaedic surgery (37%) was less than that of men (53%), which, however, increased over the study period. Students who matched in orthopaedic surgery had greater numbers of publications (3.55 [range 0 to 17] average publications) compared with students who matched in other specialties (1.98 (range 0 to 11) average publications). The average number of publications per student increased from 0.79 (±1.44, range 0 to 10, 40%) preprogram to 1.95 (±2.28, range 0 to 11, 71%) postprogram. Measured factors associated with orthopaedic surgery match were publications with program mentor, postprogram first authorship, and total publications. DISCUSSION: Approximately half of the participants matched into orthopaedic surgery. Analysis showed that research productivity increased after program participation and was statistically associated with increased likelihood of orthopaedic surgery match.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".