RESULTS OF A STEPPED EXERCISE PROGRAM AMONG VETERANS WITH KNEE OSTEOARTHRITIS AND CO-OCCURRING BACK PAIN
Bibliographic record
Abstract
Abstract Co-occurring back pain can exacerbate an already high risk of disability among patients with knee osteoarthritis (OA). We described the effect of a stepped exercise program for patients with knee OA (STEP-KOA) versus arthritis education (AE) on Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores for participants with and without co-occurring back pain. Veterans with knee OA (n= 345) were randomized 2:1 to STEP-KOA or AE control group that received mailed materials. STEP-KOA began with 3 months of internet-supported home exercise (Step 1), followed by 3 months of bi-weekly physical activity coaching (Step 2), then 3 months of physical therapy (Step 3) if participants did not make clinically relevant improvement in pain or function with the prior step. WOMAC, which assesses lower-extremity pain, stiffness and function was the primary outcome at 9 months. Self-reported back pain was reported at baseline. Differences in mean WOMAC scores at 9-months were compared between arms in those with and without back pain using t-tests. Overall, 72.4% (n=250) reported having co-occurring back pain. Among participants with co-occurring back pain (n= 176), 9-month mean WOMAC score was 10.1 points lower (95% CI -15.5, -4.7; p< 0.001) in STEP-KOA vs. AE. Among participants without co-occurring back pain (n= 79), 9-month mean WOMAC score did not differ between arms (Mean Difference= 4.3; 95% CI -5.3, 13.9; p=0.37). In conclusion, among participants with co-occurring back pain, the STEP-KOA intervention resulted in a significant reduction in mean WOMAC score at 9-months compared to AE alone.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".