The effect of patients' sex on physicians' recommendations for total knee arthroplasty
Bibliographic record
Abstract
BACKGROUND: The underuse of total joint arthroplasty in appropriate candidates is more than 3 times greater among women than among men. When surveyed, physicians report that the patient's sex has no effect on their decision-making; however, what occurs in clinical practice may be different. The purpose of our study was to determine whether patients' sex affects physicians' decisions to refer a patient for, or to perform, total knee arthroplasty. METHODS: Seventy-one physicians (38 family physicians and 33 orthopedic surgeons) in Ontario performed blinded assessments of 2 standardized patients (1 man and 1 woman) with moderate knee osteoarthritis who differed only by sex. The standardized patients recorded the physicians' final recommendations about total knee arthroplasty. Four surgeons did not consent to the inclusion of their data. After detecting an overall main effect, we tested for an interaction with physician type (family physician v. orthopedic surgeon). We used a binary logistic regression analysis with a generalized estimating equation approach to assess the effect of patients' sex on physicians' recommendations for total knee arthroplasty. RESULTS: In total, 42% of physicians recommended total knee arthroplasty to the male but not the female standardized patient, and 8% of physicians recommended total knee arthroplasty to the female but not the male standardized patient (odds ratio [OR] 4.2, 95% confidence interval [CI] 2.4-7.3, p < 0.001; risk ratio [RR] 2.1, 95% CI 1.5-2.8, p < 0.001). The odds of an orthopedic surgeon recommending total knee arthroplasty to a male patient was 22 times (95% CI 6.4-76.0, p < 0.001) that for a female patient. The odds of a family physician recommending total knee arthroplasty to a male patient was 2 times (95% CI 1.04-4.71, p = 0.04) that for a female patient. INTERPRETATION: Physicians were more likely to recommend total knee arthroplasty to a male patient than to a female patient, suggesting that gender bias may contribute to the sex-based disparity in the rates of use of total knee arthroplasty.
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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 | 0.006 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".