Effect of IoT-based power cycling and quadriceps training on pain and function in patients with knee osteoarthritis: A randomized controlled trial protocol
Bibliographic record
Abstract
BACKGROUND: Knee osteoarthritis (KOA) is a chronic musculoskeletal disease affecting the entire joint. Exercise therapy is the core treatment plan for non-surgical treatment of KOA, and tele-rehabilitation is also applied to KOA, but there is a lack of research on the comparison of pain and function recovery between different exercise methods combined Internet respectively. The study aims to compare the effects of power cycling and quadriceps training combined with online guidance separately on KOA mitigation of pain, recovery of function, quality of life, and adherence of participants in the community, compared to the control group. METHODS: This study is a single-blind, 12-week parallel randomized controlled trial. Seventy-two participants aged ≥ 50 years with KOA will be randomized into either the power cycling group, the quadriceps group or the control group. The intervention will be performed three times per week during 12 weeks. Outcome measures will be assessed at baseline, and at 4, 8, and 12 weeks after allocation. The primary outcome will be self-reported pain, assessed with the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain subscale. Secondary outcomes will include mitigation of knee pain, quality of life, improvement of functional physical performance, adherence of participants. DISCUSSION: By summarizing the study's strengths and limitations, this trial results may guide tele-rehabilitation of KOA in the community.Trial registration: The study was registered in the clinical trial registry ChiCTR2200059255, 27/04/2022.
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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 | 0.013 | 0.012 |
| 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 | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.052 | 0.007 |
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".