The effect of resistance training on patients with knee osteoarthritis: a systematic review and meta-analysis
Bibliographic record
Abstract
The objective of this study is to investigate the beneficial effects of resistance training (RT) on individuals suffering from knee osteoarthritis (KOA). In order to gather relevant studies from the beginning of various databases until January 2023, a comprehensive search was conducted on PubMed, Embase, Scopus, Web of Science, and The Cochrane Library. Additionally, manual searches were performed on the reference lists. The association between RT and KOA was analysed using a random-effects model. The results indicated that patients with KOA who underwent RT experienced a significant reduction in the WOMAC (Western Ontario and McMaster Universities Osteoarthritis) Pain index (WMD = −2.441; 95% CI = −3.610 to −1.273; p < 0.01), the WOMAC Stiffness index (WMD = −1.018; 95% CI = −1.744 to −0.293; p < 0.01), the WOMAC Function index (WMD = −7.208; 95% CI = −10.412 to −4.004; p < 0.01), and the VAS (Visual Analogue Scale) index (WMD = −5.721; 95% CI = −9.320 to −2.121; p < 0.01). These improvements were observed when compared to the control group. However, no significant difference was found in the 6-MWT (6-Minute Walk Test) index between the two groups (WMD = 2.659; 95% CI= −16.741 to 22.058; p = 0.788). Consequently, RT has the potential to positively enhance pain, stiffness, and function in patients with KOA, while the 6-MWT index may not exhibit significant improvement.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.028 |
| Bibliometrics | 0.006 | 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) | 0.003 | 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".