Traditional Chinese acupuncture versus minimal acupuncture for mild-to-moderate knee osteoarthritis: a protocol for a randomised, controlled pilot trial
Bibliographic record
Abstract
INTRODUCTION: Knee osteoarthritis (KOA) is one of the most common musculoskeletal disorders. Acupuncture is a popular form of complementary medicine for musculoskeletal conditions, although the evidence is inconclusive. Our objective is to evaluate the efficacy of traditional Chinese acupuncture for pain relief and function improvement in mild-to-moderate knee osteoarthritis (TCAKOA) participants. METHODS/ANALYSIS: 42 patients will be recruited who have been diagnosed with mild-to-moderate KOA and randomly allocated in equal proportions to traditional Chinese acupuncture or minimal acupuncture. They will receive acupuncture for 24 sessions over 8 weeks. The primary end point is success rate, which will be calculated according to a change from baseline in Western Ontario and McMaster Universities Osteoarthritis Index pain and function scores at 8 weeks. Secondary end points include pain and function measurement, global change, the quality of life and the use of non-steroidal anti-inflammatory drugs (Celebrex, Pfizer) at 8, 16 and 26 weeks. ETHICS AND DISSEMINATION: Ethical approval of this study has been granted by the Research Ethical Committee of Beijing Hospital of Traditional Chinese Medicine Affiliated to Capital Medical University (permission number: 2016BL-010-02). Written informed consent will be obtained from all participants. Outcomes of the trial will be disseminated through peer-reviewed publications. TRIAL REGISTRATION NUMBER: ISRCTN14016893; Pre-results.
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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 | 0.034 | 0.029 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.061 | 0.009 |
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".