A Randomized, Parallel Control And Multicenter Clinical Trial of Evidence-Based Traditional Chinese Medicine Massage Treatment VS External Diclofenac Diethylamine Emulgel For The Treatment of Knee Osteoarthritis
Bibliographic record
Abstract
Abstract Background: Both massage and topically administered NSAIDs are safe and effective treatment for knee osteoarthritis (KOA); however, different massage technique sects in China caused assessment difficulties for the treatment of KOA. In order to standardize massage techniques and procedures, we organized multi-disciplinary experts in China to acquire an evidence-based traditional Chinese medicine massage treatment of knee osteoarthritis. The purposes of this study are to evaluate the efficacy and safety of evidence-based traditional Chinese medicine massage treatment of KOA compared to External Diclofenac Diethylamine Emulgel.Methods and design: 300 participants diagnosed with KOA will be randomly divided into the experimental group, control group, and waiting list group in a ratio of 2:1. The participants will receive evidence-based traditional Chinese medicine massage 2 sessions per week for 10 weeks or External Diclofenac Diethylamine Emulgel 3-4 times per day for 10 weeks respectively. The MRI scans and X-ray will be performed at baseline and the end of the intervention period. The main evaluation index will include the Western Ontario and McMaster Osteoarthritis Index (WOMAC). The secondary evaluation index will include：(1) WOMAC each dimension; (2) the PRO scale for knee osteoarthritis based on the concept of Traditional Chinese Medicine（Chinese scale for knee osteoarthritis, CSKO）; (3) MRI scan and X-ray evaluation.Discussion: The results of our study will help to evaluate efficacy and safety of evidence-based traditional Chinese medicine massage treatment of KOA compared with External Diclofenac Diethylamine Emulgel combined with clinical, X-ray and MRI changes. Trial registration: Chinese Clinical Trial Registry ChiCTR1800014400. Registered on 10 January 2018.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".