Effect of Acupuncture for Methadone Reduction
Bibliographic record
Abstract
BACKGROUND: Methadone maintenance treatment (MMT) is effective for managing opioid use disorder, but adverse effects mean that optimal therapy occurs with the lowest dose that controls opioid craving. OBJECTIVE: To assess the efficacy of acupuncture versus sham acupuncture on methadone dose reduction. DESIGN: Multicenter, 2-group, randomized, sham-controlled trial. (Chinese Clinical Trial Registry: ChiCTR2200058123). SETTING: 6 MMT clinics in China. PARTICIPANTS: Adults aged 65 years or younger with opioid use disorder who attended clinic daily and had been using MMT for at least 6 weeks. INTERVENTION: Acupuncture or sham acupuncture 3 times a week for 8 weeks. MEASUREMENTS: The 2 primary outcomes were the proportion of participants who achieved a reduction in methadone dose of 20% or more compared with baseline and opioid craving, which was measured by the change from baseline on a 100-mm visual analogue scale (VAS). RESULTS: < 0.001). No serious adverse events occurred. There were no notable differences between study groups when participants were asked which type of acupuncture they received. LIMITATION: Fixed acupuncture protocol limited personalization and only 12 weeks of follow-up after stopping acupuncture. CONCLUSION: Eight weeks of acupuncture were superior to sham acupuncture in reducing methadone dose and decreasing opioid craving. PRIMARY FUNDING SOURCE: National Natural Science Foundation of China.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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".