Stretching, Bracing, and Devices for the Treatment of Osteoarthritis-Associated Joint Contractures in Nonoperated Joints: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
CONTEXT: Many patients with osteoarthritis (OA) develop range of motion (ROM) restrictions in their affected joints (contractures), associated with worse outcomes and rising healthcare costs. Effective treatment guidance for lost ROM in OA-affected joints is lacking. OBJECTIVE: A systematic review and meta-analysis evaluating the effectiveness of stretching and/or bracing protocols on native (nonoperated) joint ROM in the setting of radiographically diagnosed OA. DATA SOURCES: Seven databases, English-language. STUDY SELECTION: Studies including participants with radiographically diagnosed OA in any native joint evaluating the effect of stretching or bracing on ROM. STUDY DESIGN: Systematic review and meta-analysis. LEVEL OF EVIDENCE: Level 2. DATA EXTRACTION: Two reviewers independently screened articles for inclusion and assessed risk of bias in included trials. Primary outcomes were ROM, pain, and adverse events (AEs). RESULTS: We identified 6284 articles. A total of 9 randomized controlled trials, all evaluating the knee, met eligibility criteria. For stretching, 3 pooled studies reported total ROM, which improved by mean difference (MD) of 9.3° (95% CI 5.0°,13.5°) versus controls. Two pooled studies showed improved knee flexion ROM (MD 10.8° [7.3°,14.2°]) versus controls. Five studies were pooled for knee extension with mean improvement 9.1° [3.4°,14.8°] versus controls. Seven pooled studies showed reduced pain (standardized MD 1.9 [1.2,2.6]). One study reported improved knee extension of 3.7° [2.9°,4.5°] with use of a device. No studies used orthoses. One study reported on AEs, with none noted. Performance bias was present in all included studies, and only 3 studies clearly reported blinding of outcome assessors. Strength of evidence for primary outcomes was considered moderate. CONCLUSION: There was moderate-quality evidence that stretching is an effective strategy for improving knee total, flexion and extension ROM, and pain. Our findings suggest that stretching to regain joint ROM in OA is not futile and that stretching appears to be an appropriate conservative intervention to improve patient outcomes as part of a comprehensive knee OA treatment plan before arthroplasty.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".