Oral herbal medicines marketed in Brazil for the treatment of osteoarthritis: A systematic review and meta‐analysis
Bibliographic record
Abstract
Herbal medications are commonly used to manage symptoms associated with osteoarthritis (OA). This systematic review evaluated the effectiveness and safety of oral medications used in Brazil for the treatment of OA. Randomized clinical trials involving adults with OA treated by a herbal medicine or a control group were eligible. The primary outcomes measured were pain, physical function, swelling, stiffness and quality of life; and the secondary outcomes were adverse events, activity limitations and treatment satisfaction. Sixteen studies were included (n = 1,741 patients) in the systematic review and nine studies in the meta-analysis, representing 6 of the 13 herbal medicines studied: Boswellia serrata (n = 2), Curcuma longa (n = 3), Harpagophytum procumbens (n = 1), Salix daphnoides (n = 3), Uncaria guianensis (n = 2) and Zingiber officinale (n = 5). B. serrata was more effective than both placebo and valdecoxib for improvement of pain and physical function. No difference was observed for H. procumbens, C. longa and U. guianensis compared with control. Z. officinale showed improvement of pain over placebo. The evidence was insufficient to support the effective and safe use of these herbal medicines, because the quality of evidence of studies was low. This study guides managers of the Brazilian public health system and prescribers in decision-making regarding the use of these herbal medicines for OA. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.021 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".