Clinical appropriateness and not race predicted referral for joint arthroplasty
Bibliographic record
Abstract
OBJECTIVE: To understand the reasons behind racial disparities in the use of total joint arthroplasty (TJA), we sought to examine the predictors of time to referral to orthopedic surgery for consideration of joint replacement. METHODS: In this prospective, longitudinal study of 676 primary care clinic patients with at least a moderately severe degree of hip or knee osteoarthritis (OA), we examined the effects of race, health beliefs (i.e., perceived benefits and risks) of TJA, and clinical appropriateness of TJA on referral to orthopedic surgery. RESULTS: The sample included 255 African Americans (38%) and 421 whites (62%); 523 patients had knee OA (78%) and 153 had hip OA (22%). Subjects were 60% male, with a mean +/- SD age of 64 +/- 9 years, a mean +/- SD body mass index of 33.6 +/- 8 kg/m(2), and a mean +/- SD summary Western Ontario and McMaster Universities Osteoarthritis Index score of 56 +/- 14, suggesting moderately severe OA. At baseline, African Americans perceived fewer benefits and greater risk from TJA than whites. There were no significant racial group differences in the proportions of cases deemed clinically appropriate for TJA. After controlling for potential confounders, clinical appropriateness (hazard ratio [HR] 1.95, 95% confidence interval [95% CI] 1.15-3.32; P = 0.01) predicted referral to orthopedic surgery. Neither race (HR 1.30, 95% CI 0.94-2.05; P = 0.1) nor health beliefs (HR 1.0, P = 0.5) were associated with referral status. CONCLUSION: In this sample of primary care clinic patients, African Americans and whites were equally likely to be referred by their physicians to orthopedic surgery. Clinical appropriateness predicted future referral to orthopedic surgery, and not race or TJA-specific health beliefs.
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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 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".