Effect of acupuncture for patients with knee osteoarthritis: study protocol for a double-dummy randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Acupuncture has been used to relieve chronic pain in patients with knee osteoarthritis (KOA), but the evidence is contradictory. Therefore, we carefully designed a double-dummy randomized controlled trial (RCT) to explore the therapeutic effect of acupuncture for KOA. METHODS/DESIGN: A total of 138 eligible participants with KOA who consent to participate will be randomly divided into Groups A, B, and C in a ratio of 1:1:1. Participants in Group A will receive verum acupuncture and placebo gel, while those in Groups B and C will be treated with diclofenac diethylammon gel and sham acupuncture, sham acupuncture and placebo gel, respectively. The patients will receive 4 weeks of treatment, five times a week, including acupuncture treatment once a day for 30 min and gel treatment three times a day. The primary outcome will be the change of Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) at week 4. The secondary outcomes will include visual analog scale (VAS), Arthritis Quality of Life Measurement Scale Simplified Scale (AIMS2-SF), Beck Anxiety Inventory (BAI), Beck Depression Inventory (BDI) and Credibility/Expectancy Questionnaire. The evaluation will be performed at baseline, week 4, 8, and 12 after randomization. DISCUSSION: This double-dummy RCT used diclofenac diethylammon gel as a positive control, and the completion of this trial will provide detailed and accurate evidence of the efficacy and safety of acupuncture for KOA. TRIAL REGISTRATION: China Clinical Trials Registry No.ChiCTR2100043947. Registered on September 24, 2020. https://www.chictr.org.cn/showproj.html?proj=122536 .
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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 | 0.030 | 0.031 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.007 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.068 | 0.011 |
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".