Bibliographic record
Abstract
BACKGROUND: Massage therapy is an attractive treatment option for osteoarthritis (OA), but its efficacy is uncertain. We conducted a randomized, controlled trial of massage therapy for OA of the knee. METHODS: Sixty-eight adults with radiographically confirmed OA of the knee were assigned either to treatment (twice-weekly sessions of standard Swedish massage in weeks 1-4 and once-weekly sessions in weeks 5-8) or to control (delayed intervention). Primary outcomes were changes in the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain and functional scores and the visual analog scale of pain assessment. The sample provided 80% statistical power to detect a 20-point difference between groups in the change from baseline on the WOMAC and visual analog scale, with a 2-tailed alpha of .05. RESULTS: The group receiving massage therapy demonstrated significant improvements in the mean (SD) WOMAC global scores (-17.44 [23.61] mm; P < .001), pain (-18.36 [23.28]; P < .001), stiffness (-16.63 [28.82] mm; P < .001), and physical function domains (-17.27 [24.36] mm; P < .001) and in the visual analog scale of pain assessment (-19.38 [28.16] mm; P < .001), range of motion in degrees (3.57 [13.61]; P = .03), and time to walk 50 ft (15 m) in seconds (-1.77 [2.73]; P < .01). Findings were unchanged in multivariable models controlling for demographic factors. CONCLUSIONS: Massage therapy seems to be efficacious in the treatment of OA of the knee. Further study of cost effectiveness and duration of treatment effect is clearly warranted. .
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".