Short-term and long-term effectiveness of acupuncture and Tuina on knee osteoarthritis: study protocol for a randomized controlled trial
Bibliographic record
Abstract
Background: The effectiveness of acupuncture and tuina in treating knee osteoarthritis (KOA) is still controversial, which limits their clinical application in practice. This study aims to evaluate the short-term and long-term effectiveness of acupuncture and tuina on KOA. Methods/design: This parallel-group, multicenter randomized clinical trial (RCT) will be conducted at the outpatient clinic of five traditional Chinese medicine hospitals in China. Three hundred and thirty participants with KOA will be randomly assigned to acupuncture, tuina, or home-based exercise group with a ratio of 1:1:1. The primary outcome is the proportion of participants achieving a minimal clinically important improvement defined as a ≥ 12% reduction on the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain dimension on short term (week 8) and long term (week 26) compared with baseline. Secondary outcomes are knee joint conditions (pain, function, and stiffness), self-efficacy of arthritis, quality of life, and psychological conditions, which will be evaluated by the WOMAC score and the Patient Global Assessment (PGA), and in addition, the respondents index of OMERACT-OARSI, Short Form 12 Health Survey (SF-12), arthritis self-efficacy scale, and European five-dimensional health scale (EQ-5D). Adverse events will be collected by self-reported questionnaires predefined. Clinical trial registration: https://www.chictr.org.cn.
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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 | 0.037 | 0.032 |
| Meta-epidemiology (narrow) | 0.008 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.007 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.086 | 0.014 |
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".