Applicant Factors for Matching Into an Orthopaedic Foot and Ankle Fellowship
Bibliographic record
Abstract
Background: Over the past 2 decades, various initiatives have aimed to enhance diversity in orthopaedic surgery, promoting greater racial, ethnic, and gender equity. Building on this progress, demographic data on orthopaedic fellowship matches has been collected over the last 3 years. This study seeks to analyze trends in applicants to foot and ankle fellowships, characterize the applicant pool, and identify traits associated with successful matches. Methods: All applicant information from a fellowship application service site was obtained for match years 2022-2024. Applicants were selected only if they applied to the specific subspecialty fellowship. Gender, race, and ethnicity were recorded. Applicant factors such as Alpha Omega Alpha (AOA) status, Gold Humanitarian status, United States Medical Licensing Examination (USMLE) Step 2 score, number of applications, and number of interview invitations were used. Applicant medical school status, including allopathic, osteopathic, Canadian, and foreign medical graduate (FMG) were analyzed. χ 2 test was performed between US and FMG applicants. Univariate and multivariate binomial logistic regression was performed for FMGs. Results: There were 286 applicants, 82.8% males, 16.5% females, 133 US- and Canadian-trained graduates, and 153 FMGs. The match rate for US- and Canadian-trained graduates was 99.2% compared with FMGs, which was 43.7% and associated with lower matching rates ( P < .00001). When performing analysis in US and FMG groups independently because of multicollinearity, no factors could be associated with matching. Only when the applicant had FMG status, then the number of interview invitations were associated with matching. When the number of interviews approached 6, the likelihood of matching was >95%. All except 1 US F&A applicant matched into an F&A fellowship. During the match period, US-trained applicants were 28%-32% female, 4%-8% Black/African American, 8%-17% Asian, 65%-73% White, 2%-4% American Indian, and 2%-8% Hispanic, with no Native Hawaiian and Pacific Islanders applying. Female applicants were above representation compared to Accreditation Council for Graduate Medical Education (ACGME) numbers, but the remaining race and ethnicity applicants were within the range of current ACGME standings, which is still lower than US Census results. Conclusion: Nearly all US-trained foot and ankle applicants matched, whereas FMG applicants matched 43% of the time.
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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 | 0.002 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".