Effects of yoga exercise in reducing the risk of knee osteoarthritis and fibulin-3 levels among overweight population: a randomized controlled trial
Bibliographic record
Abstract
Background and purpose: One of the main risk factors for knee osteoarthritis is overweight. The purpose of this study is to determine the effectiveness of yoga exercise in reducing the risk of knee osteoarthritis and fibulin-3 levels in overweight populations. Methods: This study was a randomized controlled trial involving 24 participants who met the inclusion and exclusion criteria. Participants were randomly divided into two groups: group one was given yoga exercise intervention and group two was given neuromuscular coordination exercise as the control group. Risk of knee osteoarthritis measurements were carried out using The Western Ontario and McMasters University Osteoarthritis Index (WOMAC). Fibulin-3 measurement used ELISA technique using human fibulin-3 kit, FBLN3 BT-LABkit 96T E4667Hu. Results: Participants of both groups were comparable, except for age where the control group is slightly older. Pre and post-test comparison showed improvement in pain, stiffness, physical function, total WOMAC score, and fibulin-3 levels in both groups. While, the post-test comparison between the two groups showed that yoga group has better scores on all indicators compared to the control groups which was statistically significant (p≤0.001). Conclusion: Both interventions are effective in reducing the risk of knee osteoarthritis and fibulin-3 levels, but yoga showed better result. We recommend yoga training to reduce the risk of knee osteoarthritis compared to conventional training. Future research is needed to measure other knee osteoarthritis biomarkers that describe the pathological process 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".