Patient Gender Affects the Referral and Recommendation for Total Joint Arthroplasty
Bibliographic record
Abstract
BACKGROUND: Rates of use of total joint arthroplasty among appropriate and willing candidates are lower in women than in men. A number of factors may explain this gender disparity, including patients' preferences for surgery, gender bias influencing physicians' clinical decision-making, and the patient-physician interaction. QUESTIONS/PURPOSES: We propose a framework of how patient gender affects the patient and physician decision-making process of referral and recommendation for total joint arthroplasty and consider potential interventions to close the gender gap in total joint arthroplasty utilization. METHODS: The process involved in the referral and recommendation for total joint arthroplasty involves eight discrete steps. A systematic review is used to describe the influence of patient gender and related clinical and nonclinical factors at each step. WHERE ARE WE NOW?: Patient gender plays an important role in the process of referral and recommendation for total joint arthroplasty. Female gender primarily affects Steps 3 through 8, suggesting barriers unique to women exist in the patient-physician interaction. WHERE DO WE NEED TO GO?: Developing and evaluating interventions that improve the quality of the patient-physician interaction should be the focus of future research. HOW DO WE GET THERE?: Potential interventions include using decision support tools that facilitate shared decision-making between patients and their physicians and promoting cultural competency and shared decision-making skills programs as a core component of medical education. Increasing physicians' acceptance and awareness of the unconscious biases that may be influencing their clinical decision-making may require additional skills programs.
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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 | 0.008 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".