Efficacy and safety of warm needle acupuncture in knee osteoarthritis
Bibliographic record
Abstract
BACKGROUND: Knee osteoarthritis (KOA) is a chronic disease, which is also recognized as a common disease affecting the elderly. However, the application of Western medicine is limited in clinical because of its obvious adverse reactions. Warm needle acupuncture (WNA) has a long history in the treatment of KOA and is widely used in Chinese. Here we will submit a protocol to evaluate the efficacy and safety of WNA in the treatment of KOA. METHODS: We will search 5 English databases (PubMed, MEDLINE, Embase, Cochrane Library, Web of Science), 4 Chinese databases [China National Knowledge Infrastructure (CNKI), China Biology Medicine, Chinese Science and Technology Journal Database (VIP), and Wanfang database] and grey literature for randomized controlled trials of WNA in the treatment of KOA. The primary outcome measure is Western Ontario and McMaster Universities Arthritis Index (WOMAC), and the secondary outcome will include degree of knee flexion and adverse events caused by WNA, such as dizziness, nausea, abdominal pain, arrhythmia, etc. The selection of the literatures will be conducted by endnote X7 software, and we will use Review Manger V.5.3 software to conduct the meta-analysis. RESULTS: This study will provide reliable evidence for WNA in the treatment of KAO. CONCLUSION: The conclusion of this study will testify the efficacy and safety of WNA in the treatment of KAO. REGISTRATION: OSF Preregistration. 2020, October 11; osf.io/bu5qw.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.002 | 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) | 0.011 | 0.001 |
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".