Efficacy and safety of co-administration of resveratrol with meloxicam in patients with knee osteoarthritis: a pilot interventional study
Bibliographic record
Abstract
BACKGROUND AND AIM: Resveratrol shows remarkable anti-inflammatory activities in experimental models. This study aims to evaluate the effect of resveratrol, as an adjuvant with meloxicam (Mlx), on the pain and functional activity during a 90-day period and monitor the adverse effects on kidney and liver functions, lipid profile, and hematological markers. PATIENTS AND METHODS: This study was a double-blind, placebo-controlled, randomized multi-center study that involved 110 patients with knee osteoarthritis (OA) and was performed at Sulaimani City, Iraq, from December 2016 to September 2017. To assess the effects of Mlx with or without resveratrol, pain severity and functional disability were evaluated at baseline and after 90 days using the Western Ontario and McMaster Universities Osteoarthritis Index. Fasting blood was collected to evaluate the lipid profile markers, hematological picture, and liver and kidney functions, in addition to vitamin D level. RESULTS: Resveratrol significantly improves pain, functions, and associated symptoms compared with placebo. The clinical and biochemical markers indicated that 500 mg/day of resveratrol, as an adjuvant with Mlx, is safe and well tolerated by the knee OA patients. CONCLUSION: Resveratrol, as an "add-on" medication with Mlx, was superior in terms of safety and efficacy to Mlx alone for the treatment of pain and improvement of physical function in patients with knee OA.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".