The effect of electroacupuncture on opioid‐like medication consumption by chronic pain patients: A pilot randomized controlled clinical trial
Bibliographic record
Abstract
Opioid-like medications (OLM) are commonly used by patients with various types of chronic pain, but their long-term benefit is questionable. Electroacupuncture (EA) has been previously shown beneficial in reducing post-operative acute OLM consumption. In this pilot randomized controlled trial, the effect of EA on OLM usage and associated side effects in chronic pain patients was evaluated. After a two-week baseline assessment, participants using OLM for their non-malignant chronic pain were randomly assigned to receive either real EA (REA, n=17) or sham EA (SEA, n=18) treatment twice weekly for 6 weeks before entering a 12-week follow-up. Pain, OLM consumption and their side effects were recorded daily. Participants also completed the McGill Pain Questionnaire (MPQ), SF-36 and Beck Depression Inventory (BDI) at baseline, and at the 5th, 8th, 12th, 16th and 20th week. Nine participants withdrew during the treatment period with another three during the follow-up period. Intention to treat analysis was applied. At the end of treatment period, reductions of OLM consumption in REA and SEA were 39% and 25%, respectively (p=0.056), but this effect did not last more than 8 weeks after treatment. There was no difference between the two groups with respect to reduction of side effects and pain and the improvement of depression and quality of life. In conclusion, REA demonstrates promising short-term reduction of OLM for participants with chronic non-malignant pain, but such effect needs to be confirmed by trials with adequate sample sizes.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".