Acupuncture for migraine prophylaxis: a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Acupuncture is commonly used to treat migraine. We assessed the efficacy of acupuncture at migraine-specific acupuncture points compared with other acupuncture points and sham acupuncture. METHODS: We performed a multicentre, single-blind randomized controlled trial. In total, 480 patients with migraine were randomly assigned to one of four groups (Shaoyang-specific acupuncture, Shaoyang-nonspecific acupuncture, Yangming-specific acupuncture or sham acupuncture [control]). All groups received 20 treatments, which included electrical stimulation, over a period of four weeks. The primary outcome was the number of days with a migraine experienced during weeks 5-8 after randomization. Our secondary outcomes included the frequency of migraine attack, migraine intensity and migraine-specific quality of life. RESULTS: Compared with patients in the control group, patients in the acupuncture groups reported fewer days with a migraine during weeks 5-8, however the differences between treatments were not significant (p > 0.05). There was a significant reduction in the number of days with a migraine during weeks 13-16 in all acupuncture groups compared with control (Shaoyang-specific acupuncture v. control: difference -1.06 [95% confidence interval (CI) -1.77 to -0.5], p = 0.003; Shaoyang-nonspecific acupuncture v. control: difference -1.22 [95% CI -1.92 to -0.52], p < 0.001; Yangming-specific acupuncture v. control: difference -0.91 [95% CI -1.61 to -0.21], p = 0.011). We found that there was a significant, but not clinically relevant, benefit for almost all secondary outcomes in the three acupuncture groups compared with the control group. We found no relevant differences between the three acupuncture groups. INTERPRETATION: Acupuncture tested appeared to have a clinically minor effect on migraine prophylaxis compared with sham acupuncture. TRIAL REGISTRATION: Clinicaltrials.gov NCT00599586.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 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) | 0.014 | 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".