Proprioceptive Training and Outcomes of Patients With Knee Osteoarthritis: A Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Objective To describe the effects of proprioceptive training on pain, stiffness, function, and functional test outcomes among patients with knee osteoarthritis (OA). Data Sources All studies completed from 1946 to 2017 were obtained from 4 databases (PubMed, MEDLINE, CINAHL, and SPORTDiscus). Study Selection Three reviewers independently identified appropriate studies and extracted data. Data Extraction Methodologic quality and level of evidence were assessed using the Physiotherapy Evidence Database scale and Oxford Centre for Evidence-Based Medicine guidelines. The standardized mean differences (SMDs) and 95% confidence intervals (CIs) were calculated for pain, stiffness, function, and functional test outcomes. Data Synthesis Seven randomized controlled trials involving 558 patients with knee OA met the inclusion criteria. The selected studies had Physiotherapy Evidence Database scores of 6 to 8. All randomized controlled trials had an Oxford Centre for Evidence-Based Medicine level of evidence of 2. Meta-analysis of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain subscale (SMD = −0.56; 95% CI = −1.06, −0.07; P = .026), function subscale (SMD = −0.40; 95% CI = −0.59, −0.21; P < .001), and non-WOMAC walking speed test (SMD = −1.07; 95% CI = −2.12, −0.01; P = .048) revealed that proprioceptive training had significant treatment effects. Proprioceptive training was not associated with reductions in WOMAC stiffness subscale scores and did not improve non-WOMAC get-up-and-go scores. Conclusions Proprioceptive training effectively promoted pain relief and completion of functional daily activity among patients with knee OA and should be included in rehabilitation programs. Stiffness and other mobility measures were unchanged after proprioceptive training. Modified proprioceptive training programs are needed to target stiffness and improve additional physical function domains.
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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 | 0.038 | 0.083 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.049 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".