Does a home-based exercise program play any role in the treatment of knee osteoarthritis? A meta-analysis
Bibliographic record
Abstract
BACKGROUND: Knee-osteoarthritis is a very common joint disorder, affecting about 85% of the population worldwide. The effectiveness of home-based exercises is still debatable, with many studies indicating positive outcomes with few side effects, while others find them of limited utility. OBJECTIVES: To assess the role of home-based exercise (HBE) programs in the treatment of knee osteoarthritis. MATERIAL AND METHODS: Randomized controlled trials were included as per the predefined Population, Intervention, Comparison, Outcomes and Study (PICOS) criteria. Demographic summaries and event data for osteoarthritis therapy in the exercise and control groups were assessed, and comparative efficacy was evaluated using clustered graphs. The RevMan software was used to calculate the odds ratio (OR) and risk ratio of the included studies. The risk of bias was also evaluated and heterogeneity analysis was performed. RESULTS: Fifteen clinical trials performed from 2000 to 2022, with a total of 2922 osteoarthritis patients, were included in the study, according to the chosen inclusion criteria. We observed a reduction in Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores but a more marked improvement in clinical symptoms in the exercise group. The Knee Injury and Osteoarthritis Outcome Score (KOOS) increased only in the exercise group and not in the control group. We obtained a pooled OR of 0.59 (95% confidence interval (95% CI): 0.36-0.98), T2 value of 0.88, χ2 value of 185.41, degrees of freedom (df) value of 14, I2 value of 92%, and p-value <0.00001. The overall Z effect was 2.04 with a p-value of 0.04. The pooled risk ratio was 0.81 (95% CI: 0.66-0.99) with a T2 value of 0.14, χ2 value of 191.53, df value of 14, I2 value of 93%, and p-value <0.00001. CONCLUSION: The data from the studies included in this meta-analysis are in favor of the use of HBEs for the treatment of knee osteoarthritis.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.024 | 0.062 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".