Evaluating the effects of ginger extract on knee pain, stiffness and difficulty in patients with knee osteoarthritis
Bibliographic record
Abstract
The present study was aimed to evaluate the effects of ginger extract on knee pain, stiffness and difficulty in patients with knee osteoarthritis. 204 patients with knee osteoarthritis were enrolled in a randomized clinical trial. After a 1-week washing period, the groups received ginger extract (103 cases) or placebo (101 cases). A responder was defined by a reduction in pain of > 15 mm on a visual analog scale (VAS) or by 20% reduction in the mean score of each index of the Western Ontario and Mc Master Universities (WOMAC) criteria after 6 weeks. Pain reduction according to VAS was more significant in ginger group than placebo (p<0.05). Although pain reduction according to WOMAC was greater in ginger than placebo group, the difference was not statistically significant. Reduction in morning stiffness and difficulty were statistically greater in the ginger than the placebo group (p<0.05). Also there was no difference between the two groups in side effects of therapy. In conclusion, the results showed that ginger extract is effective in reducing pain, stiffness and difficulty in patients with knee osteoarthritis, therefore is recommended as a safe drug for these patients. Key words: Osteoarthritis, ginger, pain, stiffness, knee, randomized clinical trial, human.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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) | 0.004 | 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".