Racial Differences in Pain and Function Following Knee Arthroplasty: A Secondary Analysis From a Multicenter Randomized Clinical Trial
Bibliographic record
Abstract
OBJECTIVE: The assessment of racial differences in pain and function outcome following knee arthroplasty (KA) has received little attention despite very substantial literature exploring a variety of other prognostic factors. The present study was undertaken to determine whether race was associated with KA outcome after accounting for potential confounding factors. METHODS: We conducted a secondary analysis of a randomized clinical trial of 384 participants with moderate-to-high pain catastrophizing who underwent KA. Preoperative measures included race/ethnicity status as well as a variety of potential confounders, including socioeconomic status, comorbidity, and bodily pain. Outcome measures were Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain and function scales as well as performance measures. Linear mixed-effects models compared outcomes over a 1-year follow-up period for African American versus non-African American participants. RESULTS: WOMAC pain scores differences for African American versus non-African American participants averaged ~2 points in unadjusted analyses and 1-1.5 points in adjusted analyses. In adjusted analyses, follow-up WOMAC function scores differed by 6 points for African Americans compared to non-African Americans (P = 0.002). CONCLUSION: African Americans generally had worse pain, function, and performance prior to KA and worse scores after surgery, but differences were small and attenuated by ~25-50% after adjustment for potential confounding. Only WOMAC function scores showed clinically important postsurgical differences in adjusted analyses. Clinicians should be aware that after adjustment for potential confounders, African Americans have approximately equivalent outcomes compared to others, with the exception of WOMAC function score.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".