Who Leaves Orthopedics? Analyzing Attrition Among Texas Orthopedic Surgeons
Bibliographic record
Abstract
Background: Nationally, attrition rates in surgical specialties have been reported to be as high as 20-26%. As of 2019, women comprised only 6.5% of the 29,613 orthopedic surgeons. Female residents showed significantly higher overall attrition rates compared to males (5.96% vs. 2.79%, p < 0.001), with unintended attrition nearly twice as high (2.09% vs. 1.01%, p < 0.001). Attrition can result from a wide array of variables, including sex, ethnicity, education, and background. The aim of this study was to evaluate the demographic and educational factors associated with career longevity beyond training among orthopedic surgeons, with a focus on identifying patterns of attrition in underrepresented groups. Methods: Data from the Texas Medical Board Physician Database as of September 2024 was evaluated using Microsoft Excel. To determine length of practice, the date of license issuance was subtracted from the date of license expiration. Surgeons with license expirations after 2024 were excluded to maintain a focus on physicians that are no longer practicing. Entries with missing or blank data fields were excluded from the analysis. Demographic variables, including sex, medical degree type (MD v. DO), and medical school origin (International Medical Graduate (IMG) vs. U.S. medical graduate (US, Canada, Puerto Rico) were reviewed and were examined. Results: This study examined factors influencing orthopedic surgeons’ career longevity, with comparisons made using chi-square analysis. Key variables included gender, race, and educational background. Attrition was highest in the first 10 years post-licensure across all demographics. Only 10% of surgeons in this range were non-white minorities. Female surgeons had a 10-year attrition rate of 80.8% (p < 0.001), compared to 40.9% (p = 0.154) for males. White surgeons displayed no significant deviations across practice duration, especially in the first ten years of practice (884 observed vs. 913 expected, p=0.332). Minorities exhibited greater attrition earlier in their careers (Asian, p < 0.001; Black, p = 0.007). This trend reversed after 20 years when comparing IMGs at 19.4% (p < 0.001) versus U.S. graduates at 12.2% (p = 0.280). Conclusion: Female orthopedics experience disproportionately high attrition rates within the first 10 years of practice, underscoring systemic challenges such as family planning pressures, workplace discrimination and lack of support. Similarly, racial minorities show high early-career attrition rates, pointing to barriers such as discrimination and isolation within the field. IMG surgeons exhibited lower long-term attrition rates than their U.S. counterparts, potentially reflecting the resilience cultivated through more challenging training and licensure pathways. This study is limited by its inclusion of recent graduates who may inflate attrition rates and its lack of data on geographic and employment transitions. Addressing these gaps could provide a more comprehensive understanding of the factors influencing career longevity.
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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 | 0.004 | 0.013 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".