Nutraceuticals for Knee Osteoarthritis Pain Relief. Results from a Preliminary Randomised Clinical Trial
Bibliographic record
Abstract
Osteoarthritis is the most common inflammation-based joint disease. Polyphenols are plant secondary metabolites with established antioxidant and anti-inflammatory properties. Recognizing the need for holistic approaches in the management of knee osteoarthritis, we designed a two-arm, randomised clinical trial to evaluate the efficacy of a supplement rich in phenolic compounds in OA. Primary outcomes included changes in Visual Analog Scale (VAS) and Western Ontario and McMaster Universities Osteoarthritis (WOMAC) pain subscale. Secondary outcome measures were the changes in WOMAC stiffness and functionality subscales. Patients were randomised (1:1) to receive a mixture of phenolic compounds and ascorbic acid (PhAA,) or ascorbic acid (AA). Μedical history, biochemical profile and anthropometric measurements were obtained. Eighty-six patients were screened and 25 were randomly allocated in a pilot study to receive a mixture of phenolic compounds and ascorbic acid (PhAA,) or ascorbic acid (AA) adjunct to stable medical treatment. The nutraceutical supplements were well tolerated and no adverse events were reported. VAS decreased in the PhAA group (p < 0.001). Additionally, WOMAC composite score decreased significantly only in the PhAA group (p < 0.05). The WOMAC subscale of pain decreased in both treatment groups (p = 0.001 for the PhAA group, p < 0.05 for the AA group). The decrease in the subscales of stiffness and physical function was not significant for either group. A possible improvement in the quality of life of these patients using nutraceutical supplements is apparent. Although preliminary, our positive results support the hypothesis that treatment with nutraceuticals may be effective for pain relief in osteoarthritis. ClinicalTrials.gov Identifier: NCT04783792.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".