Effect of Tai Chi on knee pain and muscle strength in middle-aged and older adults with knee osteoarthritis: a randomized controlled trial protocol
Bibliographic record
Abstract
BACKGROUND: Knee osteoarthritis (KOA) is a common public health problem and a leading cause of long-term pain, decreased muscle strength, and even disability. Tai Chi has been proved effective and highly recommended for KOA management worldwide. However, little is known about its benefits on quadriceps strength which is closely associated with relieving knee pain. This trial is designed to evaluate the efficacy and safety of Tai Chi on knee pain and muscle strength in middle-aged and older adults with KOA. METHODS: A total of 100 participants will be randomly divided into a Tai Chi group (TC group) (1x/week for 12 weeks) and a control group with a health education and stretching program (1x/week for 12 weeks) with a follow-up period of 6 weeks. The primary outcome is the change of Western Ontario and McMaster Universities (WOMAC) pain subscale at week 12 compared with baseline. Secondary outcomes include WOMAC stiffness and function subscales, data from isokinetic dynamometry, gait analysis with electromyography (EMG), and a 36-item short form health survey (SF-36). The daily dose of pain-relieving medication will also be recorded. All adverse effects will be assessed by the Treatment Emergent Symptom Scale (TESS). DISCUSSION: We expect this randomized trial to evaluate the effectiveness of Tai Chi on relieving pain and increasing quadriceps strength in KOA patients. This protocol, if proven effective, will contribute to providing a promising alternative intervention for middle-aged and older adults with KOA. TRIALS REGISTRATION NUMBER: This trial has been registered in the China Clinical Trials Registry (registration number: ChiCTR2300069339).
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 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) | 0.044 | 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".