Different kinds of acupuncture treatments for knee osteoarthritis: a multicentre, randomized controlled trial
Bibliographic record
Abstract
INTRODUCTION: Knee osteoarthritis (KOA) is a chronic disease with symptoms of persistent pain or resting pain, joint stiffness, numbness, limitation of activity and even disability, with significant associated costs and effects on individuals' life quality. The use of acupuncture for the management of chronic pain is receiving increasing recognition from both the public and professionals. The aim of this study is to identify the effects of three commonly used acupuncture treatments for KOA. METHODS/ANALYSIS: In a prospective trial involving six hospitals in Zhejiang Province (China), 360 patients with KOA will be included. Eligible patients will be randomized into six groups: Acupuncture, Electro-acupuncture, Mild moxibustion, Warm-needling, Sham acupuncture and Celebrex treatment. Twelve treatment sessions will be performed over a 4-week period. The primary outcome will be the visual analogue scale and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) function scores (the average of the past 3 days) at weeks 2 and 4 and at 3-month and 6-month follow-up. Secondary outcome measures will be as follows: the WOMAC pain score and WOMAC stiffness score (the average of the past 3 days); the Physical Activity Scale of the Elderly (PASE); knee joint swelling measurement; the WHO Quality Of Life-BREF (WHOQOL-BREF) life quality scale; and the incidence of adverse events. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03563690. Registered on 2rd July 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".