Traditional Chinese medicine therapies for patients with knee osteoarthritis: A protocol for systematic review and network meta-analysis
Bibliographic record
Abstract
BACKGROUND: Knee osteoarthritis (KOA) is a common cause of chronic musculoskeletal pain and disability as well as a socioeconomic burden on healthcare services globally. Numerous clinical trials indicated that traditional Chinese medicine (TCM) may effectively improve the clinical symptoms of KOA patients. However, the comparative efficacy and safety of different TCM therapies in patients with KOA is not yet clear. In order to evaluate the efficacy and safety of TCM for KOA, we will conduct a systematic review and network meta-analysis on the existing randomized controlled trials (RCTs). METHODS: A systematic literature search will be conducted in PubMed, Web of Science, Embase, EBSCO, Cochrane Library, China National Knowledge Infrastructure, Wanfang, Chinese Biomedical Literature Database, and the VIP Database for Chinese Technical Periodicals up to February 2022 to identify the relevant RCTs. The primary outcomes are visual analog scale, Western Ontario and McMaster Universities Osteoarthritis Index, Lysholm score, and Lequesne index. Secondary outcomes include the total clinical effective rate and adverse events. Study quality will be evaluated using the Cochrane risk of bias tool (RoB 2.0) for RCTs. Data analysis will be performed using Stata and WinBUGS. The quality of evidence will be assessed using the Grades of Recommendations Assessment Development and Evaluation. RESULTS: The results of this study will be submitted to a peer-reviewed journal for publication. CONCLUSIONS: This study will provide evidence-based medical evidence for the treatment of KOA with TCM therapies and offer better assistance for clinical practice. PROTOCOL REGISTRATION NUMBER: INPLASY202230008.
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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 | 0.067 | 0.085 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.019 | 0.031 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.003 |
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".