The effects of acupuncture on withdrawal symptoms in recovering substance abusers
Bibliographic record
Abstract
Background: According to Stats Canada, 6 million Canadians meet the criteria for a substance use disorder. Withdrawal symptoms include any physical or psychological discomfort experienced upon cessation of substance use, such as hallucinations, seizures, mild headaches, trouble sleeping or increased agitation. There have been various anecdotal and documented benefits of the use of acupuncture to treat withdrawal symptoms, but more research is needed to explore this relationship. Objective: To determine the effects of acupuncture on withdrawal symptoms in recovering substance abusers. Methods: We completed a structured literature review to investigate the effects of acupuncture of withdrawal symptoms. Results: Of all of the studies examined (n=16), seven studies reported that acupuncture significantly improved withdrawal symptoms compared to the control group. Nine studies reported no difference between the treatment and control groups. Discussion: Results indicate low levels of association between treatment methods and outcome measures. Several biases and confounders were inherent of the treatment methods and study population. Limitations included only searching two databases and potentially too stringent of inclusion criteria. Future studies should explore ways to increase blinding techniques, as well as select specific drugs and withdrawal symptoms to analyze. Conclusion: The majority of studies indicated no difference between the experimental and control groups. The significance of these findings suggest that more research is needed, and until more compelling results emerge, acupuncture may not be considered evidence-based.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".