Effects of intensity of electroacupuncture on chronic pain in patients with knee osteoarthritis: a randomized controlled trial
Bibliographic record
Abstract
Abstract Background Conditioned pain modulation (CPM) is impaired in people with chronic pain such as knee osteoarthritis (KOA). The purpose of this randomized, controlled clinical trial was to investigate whether strong electroacupuncture (EA) was more effective on chronic pain by strengthening the CPM function than weak EA or sham EA in patients with KOA. Methods In this multicenter, three-arm parallel, single-blind randomized controlled trial, 301 patients with KOA were randomly assigned. Patients were randomized into three groups based on EA current intensity: strong EA (> 2 mA), weak EA (< 0.5 mA), and sham EA (non-acupoint). Treatments consisted of five sessions per week, for 2 weeks. Primary outcome measures were visual analog scale (VAS), CPM function, and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Results Three hundred one patients with KOA were randomly assigned, among which 271 (90.0%) completed the study (mean age 63.93 years old). One week of EA had a clinically important improvement in VAS and WOMAC but not in CPM function. After 2 weeks treatment, EA improved VAS, CPM, and WOMAC compared with baseline. Compared with sham EA, weak EA (3.8; 95% CI 3.45, 4.15; P < .01) and strong EA (13.54; 95% CI 13.23, 13.85; P < .01) were better in improving CPM function. Compared with weak EA, strong EA was better in enhancing CPM function (9.73; 95% CI 9.44, 10.02; P < .01), as well as in reducing VAS and total WOMAC score. Conclusion EA should be administered for at least 2 weeks to exert a clinically important effect on improving CPM function of KOA patients. Strong EA is better than weak or sham EA in alleviating pain intensity and inhibiting chronic pain. Trial registration This study was registered with the Chinese Clinical Trial Registry ( ChiCTR-ICR-14005411 ), registered on 31 October 2014.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.003 | 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".