Fire acupuncture for mild to moderate knee osteoarthritis: a protocol for a randomized controlled pilot trial
Bibliographic record
Abstract
BACKGROUND: Knee osteoarthritis (KOA) is one of the most common bone and joint diseases. As one of the main non-drug therapies, acupuncture is widely used to treat KOA, although the evidence for its efficacy is inconclusive. The objective of this pilot trial is to clarify the clinical efficacy and safety of fire acupuncture in the treatment of mild to moderate KOA and to provide high-quality data for further research. METHODS/DESIGN: This study is a prospective randomized controlled pilot trial in which 120 patients with mild to moderate KOA will be randomly allocated in equal proportions to a fire acupuncture group or a general acupuncture group. They will receive acupuncture for six sessions over 2 weeks. The primary end point is success rate, which will be calculated based on the change from baseline of the pain and function scores in the Western Ontario and McMaster Universities Osteoarthritis Index at 4 weeks. Secondary end points include the proportion of patients achieving clinical improvement based on: (1) the OMERACT-OARSI responder criteria, (2) levels of matrix metalloproteinase 3, interleukin 1β, and tumor necrosis factor α in blood, and (3) a subjective efficacy evaluation from patients. TRIAL REGISTRATION: Chinese Clinical Trial Registry, ChiCTR1800019162. Registered on 29 October 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 | 0.052 | 0.034 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.056 | 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".