The Geographic Movement Patterns and Career and Personal Interests of Orthopaedic Surgery Residents in the United States
Bibliographic record
Abstract
BACKGROUND: Orthopaedic surgery continues to be a highly desired residency specialty for graduating medical students in the United States. The geographic preferences and trajectories of orthopaedic surgery applicants are not well understood. OBJECTIVE: The primary objective of this study was to determine the geographic movement patterns of orthopaedic residents from university through residency. A secondary objective was to trend the career and personal interests of orthopaedic residents. METHODS: One hundred eighty-seven orthopaedic surgery programs and 3672 residents were identified through the Electronic Residency Application Service website and Google searches and included for study. Program provided information, including the residents' medical school, undergraduate institution, career interests, and personal interests was then gathered. All data were recorded between the dates of March 25, 2020, and April 2, 2020. RESULTS: A minority of orthopaedic residents (46.2%; n = 1,569/3,398) were training in the same geographic region of their medical school; however, analysis revealed a statistically significant strength of association for home region over a different US census bureau region (χ2 = 127.4, df = 8, Cramer's V = 0.2, P < 0.001). The average distance between orthopaedic residents' medical school and residency program was 666 miles. Male residents were more interested in arthroplasty, spine, and sports, whereas female residents were more interested in hand and pediatrics. The residents leading interests were in arthroplasty (24.4%), sports (21.7%), and trauma (21.3%). CONCLUSION: Orthopaedic surgery residents are more likely to train in a geographical region that is different from their medical school or undergraduate institution. The reported career interests of male and female orthopaedic residents showed significant differences, but personal interests seem to be similar between genders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".