The effect of curcuminoids for treating knee osteoarthritis
Bibliographic record
Abstract
BACKGROUND: Knee osteoarthritis (KOA) is 1 of the commonest cause of disability with joint pain in adults and a burden on healthcare resources. The limitations of current KOA treatment necessitate further researches to discover the more efficacious and safety treatments. There are increasing clinical studies investigating the potential protective effects of Curcuminoids in the alleviation of symptoms in patients suffering from KOA. However, the convincing evidence indicating the efficacy of curcuminoids for patients suffering from KOA remains unclear. METHODS: Several databases including PubMed, Web of Science, Cochrane Library, Embase, Chinese Biomedical Literature Database, Chinese National Knowledge Infrastructure, and Wanfang Database will be searched. And the language was not limited. We will include all Randomized controlled trials that use curcuminoids to treat patients with KOA, regardless of blinding. If the pre-crossover data can be analyzed to avoid carryover effects, the crossover randomized trials also are included. Meanwhile, We will exclude non-randomized controlled trials, qualitative studies, uncontrolled clinical trials and laboratory studies. The primary end point include Western Ontario and McMaster Universities Osteoarthritis Index, visual analog scale scores and Lequesne's pain functional index. The secondary end points are total effective rate and adverse effects. The Review Manager Version 5.3 will be used to perform the data synthesis and subgroup analysis. DISCUSSION: There are evidences that supports the potential protective effects of Curcuminoids in the alleviation of symptoms in patients suffering from KOA. This systematic review and meta-analysis would provide convincing evidence indicating that curcuminoids relieve the symptoms of patients suffering from KOA. REGISTRATION: Open Science Framework (OSF) registries (https://osf.io/fz29b) with the registration DOI: 10.17605/OSF.IO/FZ29B.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".