Effectiveness of Yijinjing exercise in the treatment of early-stage knee osteoarthritis: a randomized controlled trial protocol
Bibliographic record
Abstract
INTRODUCTION: Knee osteoarthritis (KOA) is still a challenging degenerative joint disease with high morbidity and disease burden. Early-stage KOA, the focus of this study, could present a Window of Opportunity to arrest the disease process and reduce the disease burden. Yijinjing exercise is an important part of physical and psychological therapies in Traditional Chinese Exercise and may be an effective treatment. However, there is no clinical efficacy assessment of Yijinjing exercise for patients with early-stage KOA. Therefore, we designed a randomised controlled trial to evaluate the effectiveness of Yijinjing exercise on patients with early-stage KOA. METHODS AND ANALYSIS: This is a parallel-design, two-arm, analyst assessor-blinded, randomised controlled trial. In total, 60 patients with early-stage KOA will be recruited and randomly assigned to the Yijinjing exercise group (n=30) and health education group (n=30) at a ratio of 1:1, receiving 12 weeks of Yijinjing exercise or health education accordingly. The primary outcome will be measured with the Western Ontario and McMaster Universities Osteoarthritis Index, and the secondary outcomes will include the Visual Analogue Scale, Short-Form 36 Item Health Survey Questionnaire, Beck Depression Inventory, Perceived Stress Scale, Berg Balance Scale, and Gait Analysis for a comprehensive assessment. Outcome measures are collected at baseline, at 12 week ending intervention and at the 12 week, 24 week and 48 week ending follow-up. The primay time point will be 12 weeks postintervention. Adverse events will be recorded for safety assessment. ETHICS AND DISSEMINATION: This study has been approved by the ethical application of the Shanghai Municipal Hospital of Traditional Chinese Medicine Ethics Committee (2021SHL-KY-78). TRIAL REGISTRATION NUMBER: ChiCTR2200065178.
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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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.047 | 0.006 |
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".