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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.008 |
| 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; both teacher heads agree on what is shown here.
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".