Efficacy and safety comparison of infrared laser moxibustion and traditional moxibustion in knee osteoarthritis: study protocol for a Zelen-design randomized controlled non-inferiority clinical trial
Bibliographic record
Abstract
BACKGROUND: Knee osteoarthritis (KOA) is the most common chronic degenerative joint disease and places a substantial burden on the public health resources in China. The purpose of this study is to preliminarily evaluate whether infrared laser moxibustion (ILM) is non-inferior to traditional moxibustion (TM) in the treatment of KOA. MATERIALS AND METHODS: In the designed Zelen-design randomized controlled non-inferiority clinical trial, a total of 74 patients with KOA will be randomly allocated to one of two interventions: ILM treatment or TM treatment. All participants will receive a 6-week treatment and a follow-up 4 weeks after treatment. The primary outcomes will be the mean change in pain scores on the numeric rating scale (NRS) measured at baseline and the end of last treatment at week 6. The secondary outcomes will be the pain scores on the NRS from weeks 1 to 5 after the start of treatment and the changes from baseline to endpoints (weeks 6 and 10) in the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), SF-36, knee circumference, and 6-min walking test. In addition, safety assessment will be performed throughout the trial. CONCLUSION: The results of our study will help determine whether a 6-week treatment with ILM is non-inferior to TM in patients with KOA, therefore providing evidence to verify if ILM can become a safer alternative for TM in clinical applications in the future. TRIAL REGISTRATION: Clinical Trial Registration Platform (ChiCTR2200065264); Pre-results. Registered on 1 November 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 | 0.027 | 0.021 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 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 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.005 |
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".