The Competitive Orthopaedic Trauma Fellowship Applicant: A Program Director's Perspective
Bibliographic record
Abstract
INTRODUCTION: In 2018, orthopaedic trauma had the lowest match rate among orthopaedic subspecialties. The purpose of this study was to determine the importance of factors evaluated by orthopaedic trauma fellowship directors when ranking applicants after the interview. METHODS: An electronic survey was submitted to fellowship directors and consisted of 16 factors included in a fellowship application. Respondents were asked to rate the importance of these factors for applicants they interviewed on a 1 to 5 Likert scale, with 1 being not at all important and 5 being critical. RESULTS: Thirty-seven fellowship directors responded (63.8%). The highest-rated factor was the applicant interview (mean score 4.82), followed by the quality of letters of recommendation (4.69), personal connections made to the applicant (3.89), and potential to be leader (3.86). Fellowship directors at academic programs rated interest in an academic career (P = 0.003), research experience (P = 0.023), and exposure to well-known orthopaedic traumatologists (P = 0.003) higher than their counterparts at private institutions. Programs with more than one fellow rated potential to be a leader higher than programs with one fellow (P = 0.02). DISCUSSION: Trainees may use this study when compiling an application to optimize their chances of matching at the program of their choice.
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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 | 0.006 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".