Effects of mind-body therapies in knee osteoarthritis: a systematic review and network meta-analysis
Bibliographic record
Abstract
Introduction: Knee osteoarthritis (KOA) is a frequent degenerative condition. Patients with KOA have employed mind-body therapies frequently, and their efficacy has been established. The main purpose of our study is to compare the effects of different mind-body therapies on patients with KOA. Material and methods: Only randomized controlled trials (RCTs) were gathered through searches in the Cochrane Library, PubMed, EBSCO, Web of Science, Medline, and Embase to investigate the effects of various mind-body therapies in KOA patients. The Cochrane bias risk assessment tool was used to assess the methodological quality of the included studies, and the data analysis program was then used to analyse the data in accordance. Results: The study used an overall sample size of 859 from a total of 17 RCTs. In addition, a total of 13 different mind-body therapies were included in our study. According to the network meta-findings, the patients' scores on the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) can be significantly improved by Baduanjin exercises (SUCRA: 98.3%), the Time Up and Go (TUG) Test can be significantly decreased by balance training (SUCRA: 99.4%), the 36-Item Short Form Health Survey questionnaire's mental health component can be significantly improved by MBSR (SUCRA: 87.9%), and balance training can significantly improve patients' physical health component of SF-36 (SUCRA: 90.3%). Conclusions: According to the Network's Ranking Plot, Baduanjin or Balance Training and MBSR can offer patients with KOA greater benefits for motor function or quality of life than other mind-body therapies. The medical staff can choose different mind-body therapies according to the patient's actual condition.
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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 | 0.024 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.039 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| 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".