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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".