Effect of Shi-Style Steaming and Bathing Decoction in Patients with Knee Osteoarthritis: Study Protocol for a Randomized Placebo-Controlled Trial
Bibliographic record
Abstract
Purpose: To prove more accurately that Chinese herbal bath therapy may be a safe, effective, simple alternative treatment modality for knee OA, we designed a randomized, double-blind, placebo-controlled trial to explore the effectiveness of SSBD for the relief of pain, daily activities, and quality of life in patients with knee OA. Patients and Methods: A single-center, 52-week, randomized controlled trial of SSBD versus placebo is being performed. A total of 200 patients with symptomatic knee OA will be randomly allocated to the SSBD treatment or placebo intervention group for 4 weeks. The two groups of patients are allowed to steam and bathe their knees once every other day, using one packet of SSBD each time, for 30 minutes, 3 times a week, for a total of 4 weeks. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain subscale at 4 weeks is the primary outcome measure, and the secondary outcomes include WOMAC stiffness and function scores, the Lysholm knee scale score, quality of life, the Brief Pain Inventory score, the Patient's Global Impressions of Improvement Scale score and the Clinical Global Impressions of Severity scale score. The safety of the herbal medications will also be evaluated. Conclusion: We will discuss whether SSBD has greater advantages in terms of efficacy, safety, and patient overall perception than does placebo control in middle-aged and elderly patients with knee OA. The findings may provide new and valuable information about the efficacy and safety of Chinese herbal bath therapy in the treatment of knee osteoarthritis.
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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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.051 | 0.010 |
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".