Pregnancy and Childbearing for Orthopaedic Surgeons
Bibliographic record
Abstract
ABSTRACT: While female representation within surgical specialties is increasing, the field of orthopaedic surgery remains male-dominated. Residency, fellowship, and early career coincide with the childbearing years of female surgeons. Given the overlap between these critical career stages and years of childbearing, there has been a rise in articles characterizing the experiences and perceptions around childbearing and its impact on surgeons and their careers. Multiple studies have reported the alarmingly high rates of pregnancy complications, infertility, pregnancy loss, voluntary delay in childbearing, and postpartum depression in surgeons, including those in the field of orthopaedic surgery. However, perinatal complications are not the only barriers female orthopaedic surgeons may face should they decide to start a family alongside their career. Negative perceptions and lack of support from their colleagues and institutions have also been reported as commonplace. Limited but successful support programs, policies, and resources that are designed to support female surgeons and their partners have been created in North America. Successful support programs can be used to inspire institutional policies across North America to hopefully improve the pregnancy and childbirth experiences of orthopaedic surgeons.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".