Evaluation the efficacy and safety of fire needle compared to filiform needle on knee osteoarthritis: study protocol for a randomised controlled trial
Bibliographic record
Abstract
Abstract Background: Knee osteoarthritis is a common clinical chronic degenerative disease associated with high morbidity and long-term disability. Previous studies have confirmed the efficacy of acupuncture on knee osteoarthritis. Fire needle acupuncture is a combination of heat and acupuncture, which may be more effective than the commonly used filiform needle acupuncture. This study is designed as a randomized controlled trial to evaluate the efficacy and safety of fire needle acupuncture compared to filiform needle acupuncture in knee osteoarthritis patients. Methods and analysis: A total of 100 participants will be randomly assigned to two different groups. Participants will receive fire-needle acupuncture treatment in the fire needle group, while participants in the filiform needle group will be treated with a filiform needle at the same acupuncture points as the fire needle group. All participants will receive a 6-weeks of treatment (2 times per week). The primary outcome is the change of the Western Ontario and McMaster Universities Osteoarthritis Index, and the secondary outcomes include the change of the Visual Analogue Scale, and 12-item Short Form Health Survey from baseline to endpoint. Ethics and dissemination: Ethical approval of this study was granted by the Research Ethical Committee of Beijing Hospital of Traditional Chinese Medicine Affiliated to Capital Medical University (2018SB-066). Written informed consent will be obtained from all participants. Outcomes of the trial will be disseminated through peer-reviewed publications. Trial registration number: ChiCTR1800019579
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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 | 0.039 | 0.037 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.076 | 0.012 |
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".