Comparison of kidney-tonifying and blood-activating medicinal herbs vs NSAIDs in patients with knee osteoarthritis
Bibliographic record
Abstract
BACKGROUND: Knee osteoarthritis (KOA) is one of the most common chronic muscular diseases in old people. In recent years, people are more and more interested in the use of Chinese herbal medicine (CHM) in the treatment of KOA, such as kidney-tonifying and blood-activating medicinal herbs (KTBAMs) in the treatment of KOA. Many studies have confirmed that KTBAMs are effective in the treatment of KOA. However, it is still unknown whether KTBAMs and NSAIDs are more effective in the treatment of KOA. Therefore, we evaluated the efficacy and safety of KTBAMs and NSAIDs in the treatment of KOA. METHODS: Randomized controlled trials (RCTs) from online databases including PubMed, Embase, the Cochrane Library, China National Knowledge Infrastructure, Chinese Scientific Journal Database, Wanfang Data, and Chinese Biomedical Literature Database that compared the efficacy of KTBAMs and NSAIDs in the treatment of KOA were retrieved. The main outcomes included the evaluation of functional outcomes, pain and adverse effects. The Cochrane risk of bias (ROB) tool was used to assess methodological quality. RESULTS: The literature will provide a high-quality analysis of the current evidence supporting KTBAMs for KOA based on various comprehensive assessments including the total effective rate, visual analog scale scores, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Lequence scores, Knee Society Scale (KSS) scores, and adverse effects. CONCLUSION: This proposed systematic review will provide up-to-date evidence to assess the effect of KTBAMs in the treatment for patients with KOA. RESEARCH REGISTRY REGISTRATION NUMBER: : reviewregistry 783.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".