Comparative efficacy and safety of Chinese herbal medicine for knee osteoarthritis
Bibliographic record
Abstract
BACKGROUND: Knee osteoarthritis (OA) is a major public health concern causing chronic disability as well as a substantial burden on health care and the economy. However, effective treatments for knee OA were still not available. Numerous clinical studies have suggested that Chinese herbal medicine (CHM) seems to be clinically effective in treating knee OA. Thus, this study aims to evaluate the efficacy and safety of CHM in the treatment of knee OA through a systematic review and network meta-analysis. METHODS: A comprehensive search will be performed in PubMed, Cochrane Library, Embase, Web of Science, China National Knowledge Infrastructure, VIP Database, Wanfang Database, Chinese Biomedical Database, and 3 clinical trials registration websites, from the database inception to May 2021. Randomized controlled trials meeting the eligible criteria based on the PICOS framework will be included. All studies fulfilling the eligible criteria will be assessed for risk of bias using the Cochrane Collaboration's tool. The primary outcome will be the visual analog scale (VAS), Western Ontario and McMaster Universities Osteoarthritis Index, and total effective rate. The secondary outcome is the incidence of adverse events. Data analysis will be performed using Stata, Addis, and WinBUGS. DISCUSSION: This study will provide a reliable evidence to assess effectiveness and safety of CHM for knee OA, which may provide guidance for clinical practice. SYSTEMATIC REVIEW REGISTRATION: This study protocol has been registered on INPLASY202160060.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.003 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".