Systematic scoping review of interactions between analgesic drug therapy and mindfulness-based interventions for chronic pain in adults: current evidence and future directions
Bibliographic record
Abstract
Most patients with chronic pain do not find adequate pain relief with a single treatment, and accumulating evidence points to the added benefits of rational combinations of different treatments. Given that psychological therapies, such as mindfulness-based interventions (MBIs), are often delivered in conjunction with concomitant analgesic drug therapies (CADTs), this systematic scoping review examines the evidence for any interactions between MBIs and CADTs. The protocol for this review has been published and registered. MEDLINE, Cochrane Central Register of Controlled Trials, EMBASE, and PsycINFO databases were searched until July 2019. We included randomized controlled trials that evaluated the efficacy of MBIs for the treatment of chronic pain. A total of 40 randomized controlled trials (2978 participants) were included. Thirty-nine of 40 (97.5%) included mindfulness-based clinical trials allowed the use of CADTs. However, only 6 of these 39 (15.4%) trials provided adequate details of what these CADTs were, and only 4 (10.3%) trials controlled for CADTs. Of great relevance to this review, none of the included trials analyzed the interactions between MBIs and the CADTs to determine whether they have an additive, synergistic, or antagonistic effect on chronic pain. Adverse events were inconsistently reported, and no judgment could be made about safety. Future trials assessing the interactions between MBIs and CADTs, with better harms reporting, are needed to better define the role of MBIs in the management of chronic pain.
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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 | 0.023 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".