Clinical Efficacy of Electroacupuncture in the Treatment of Chronic Neck Pain: A Randomized Clinical Trial
Bibliographic record
Abstract
Purpose: Chronic neck pain (CNP) is a common but challenging symptom in clinical practice. Acupuncture is widely used in alleviating the symptoms of CNP. The main objective of this study was to evaluate the efficacy and safety of electroacupuncture (EA) in patients with CNP and to quantify the specific effects of EA by controlling for placebo effects. Patients and Methods: A randomized sham-controlled trial was conducted at the outpatient departments of single hospital in China from November 2019 to November 2020 and a total of 105 participants with CNP were enrolled. Participants were randomly assigned (1:1:1) to the EA group, sham electroacupuncture (SEA) group and waiting list (WL) group. The primary outcome was change in the Northwick Park Neck Pain Questionnaire (NPQ). Secondary outcomes included McGill Pain Questionnaire (MG), visual analogue scale (VAS) and pain threshold (PT). Results: This randomized clinical trial included 98 patients. The EA group demonstrated a greater reduction in NPQ scores compared to the SEA group after 10 sessions (− 7.2564, 95% CI=− 12.2875 to − 2.2253, P =0.0054) and at 3-month follow up (− 7.0090, 95% CI=− 10.5039 to − 3.5140, P =0.0002). After 10 sessions, the EA and SEA groups exhibited greater reductions in NPQ scores compared to the WL group (EA vs WL: P < 0.001, [95% CI=6.570 to 15.503]; SEA vs WL: P =0.027, [95% CI=0.578 to 9.580]). However, the EA group achieved clinically significant NPQ improvements (> 25%), whereas the SEA group failed to meet this criterion. Conclusion: This randomized clinical trial found that, in patients with CNP, EA significantly improved the symptoms compared with SEA and WL groups both immediately and cumulatively (at 5 weeks), and these benefits persisted through week 17. These comparisons demonstrated that EA’s clinical benefits exceeded placebo effects. Keywords: electroacupuncture, chronic neck pain, randomized controlled trial, placebo effect, analgesic effect
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| 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) | 0.007 | 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".