The effect of pomegranate juice on clinical signs, matrix metalloproteinases and antioxidant status in patients with knee osteoarthritis
Bibliographic record
Abstract
BACKGROUND: Osteoarthritis (OA) is one of the commonest forms of musculoskeletal disorders that leads to joint degeneration and has a major impact on patients' quality of life. Experimental and in vitro studies have suggested the protective roles of pomegranate juice (PJ) as a rich antioxidant source for mitigating cartilage inflammation. In this interventional study, 38 patients with knee OA were randomly divided into two groups: PJ or control for 6 weeks to evaluate the effect of this intervention on clinical signs, inflammation and antioxidant status. RESULTS: Significant decreases in Western Ontario and McMaster Universities Osteoarthritis index (WOMAC) total score (P = 0.01), stiffness score (P = 0.00) and physical function score (P = 0.01) were observed in PJ group after the intervention. The means of serum levels of matrix metalloproteinase (MMP)-13 was significantly decreased (P = 0.02) and glutathione peroxidase was increased in the intervention group compared with the control group after the study period (P = 0.02). CONCLUSIONS: According to the findings of this clinical trial, PJ consumption can improve physical function and stiffness, decrease breakdown cartilage enzymes and increase antioxidant status in patients with knee OA. © 2016 Society of Chemical Industry.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".