Does Integrative Medicine Reduce Prescribed Opioid Use for Chronic Pain? A Systematic Literature Review
Bibliographic record
Abstract
BACKGROUND: Chronic pain (CP) is a major public health problem. Many patients with CP are increasingly prescribed opioids, which has led to an opioid crisis. Integrative medicine (IM), which combines pharmacological and complementary and alternative medicine (CAM), has been proposed as an opioid alternative for CP treatment. Nevertheless, the role of CAM therapies in reducing opioid use remains unclear. OBJECTIVES: To explore the effectiveness of the IM approach or any of the CAM therapies to reduce or cease opioid use in CP patients. METHODS: An online search of MEDLINE and Embase, CINAHL, PubMed supp., and Allied and Complementary Medicine Database (AMED) for studies published in English from inception until February 15, 2018, was conducted. The Mixed Methods Appraisal Tool (MMAT) was used to critically appraise selected studies. RESULTS: The electronic search yielded 5,200 citations. Twenty-three studies were selected. Eight studies were randomized controlled trials, seven were retrospective studies, four studies were prospective observational, three were cross-sectional, and one was quasi-experimental. The majority of the studies showed that opioid use was reduced significantly after using IM. Cannabinoids were among the most commonly investigated approaches in reducing opioid use, followed by multidisciplinary approaches, cognitive-behavioral therapy, and acupuncture. The majority of the studies had limitations related to sample size, duration, and study design. CONCLUSIONS: There is a small but defined body of literature demonstrating positive preliminary evidence that the IM approach including CAM therapies can help in reducing opioid use. As the opioid crisis continues to grow, it is vital that clinicians and patients be adequately informed regarding the evidence and opportunities for IM/CAM therapies for CP.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".