Multidisciplinary care for opioid dose reduction in patients with chronic non-cancer pain: A systematic realist review
Bibliographic record
Abstract
CONTEXT: Opioid related deaths are at epidemic levels in many developed nations globally. Concerns about the contribution of prescribed opioids, and particularly high-dose opioids, continue to mount as do initiatives to reduce prescribing. Evidence around opioid tapering, which can be challenging and potentially hazardous, is not well developed. A recent national guideline has recognized this and recommended referral to multidisciplinary care for challenging cases of opioid tapering. However, multidisciplinary care for opioid tapering is not well understood or defined. OBJECTIVE: Identify the existing literature on any multidisciplinary care programs that evaluate impact on opioid use, synthesize how these programs work and clarify whom they benefit. STUDY DESIGN: Systematic rapid realist review. DATASET: Bibliographic databases (MEDLINE, EMBASE, CINAHL, PsycINFO, Cochrane Library), grey literature, reference hand search and formal expert consultation. RESULTS: 95 studies were identified. 75% of the programs were from the United States and the majority (n = 62) were published after 2000. A minority (n = 23) of programs reported on >12 month opioid use outcomes. There were three necessary but insufficient mechanisms common to all programs: pain relief, behavior change and active medication management. Programs that did not include a combination of all three mechanisms did not result in opioid dose reductions. A concerning 20-40% of subjects resumed opioid use within one year of program completion. CONCLUSIONS: Providing alternative analgesia is insufficient for reducing opioid doses. Even high quality primary care multidisciplinary care programs do not reduce prescribed opioid use unless there is active medication management accomplished by changing the primary opioid prescriber. Rates of return to use of opioids from these programs are very concerning in the current context of a highly potent and lethal street drug supply. This contextual factor may be powerful enough to undermine the modest benefits of opioid dose reduction via multidisciplinary care.
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 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 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".