Acupuncture Randomized Trials (ART) in Patients with Chronic Low Back Pain and Osteoarthritis of the Knee – Design and Protocols
Bibliographic record
Abstract
BACKGROUND: We report on the study design and protocols of two randomized controlled trials (Acupuncture Randomized Trials = ART) that investigate the efficacy of acupuncture in the treatment of chronic low back pain and osteoarthritis of the knee, respectively. OBJECTIVE: To investigate whether acupuncture is more efficacious than (a) no treatment or (b) minimal acupuncture in the treatment of low back pain and osteoarthritis. DESIGN: Two randomized, controlled, multicenter trials with three treatment arms and a total follow-up time of 52 weeks. SETTING: 30 practitioners and outpatient units in Germany specialized in acupuncture treatment. PATIENTS: 300 patients will be included in each study. In the low back pain trial, patients will be included according to clinical diagnosis. In the osteoarthritis pain trial, patients will be included according to the American College of Rheumatology criteria. INTERVENTIONS: Patients are randomly assigned to receive either (1) semi-standardized acupuncture (150 patients), (2) minimal acupuncture at non-acupuncture points (75 patients), or (3) no treatment for two months followed by semi-standardized acupuncture (75 patients, waiting list control). Acupuncture treatment consists of 12 sessions per patient over a period of 8 weeks. MAIN OUTCOME MEASURE: The main outcome measure is the difference between baseline and the end of the 8-week treatment period in the following parameters: pain intensity as measured by a visual analogue scale (VAS; 0-100 mm) in the low back pain trial and by the Western Ontario and McMaster Universities Osteoarthritis Score (WOMAC) in the osteoarthritis trial. OUTLOOK: The results of these two studies (available in 2004) will provide health care providers and policy makers with the information needed to make scientifically sound assessments of acupuncture therapy.
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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 | 0.134 | 0.150 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".