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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".