Efficacy of interventions targeted at physician prescribers of opioids for chronic non-cancer pain: an overview of systematic reviews
Bibliographic record
Abstract
BACKGROUND: To combat the opioid crisis, interventions targeting the opioid prescribing behaviour of physicians involved in the management of patients with chronic non-cancer pain (CNCP) have been introduced in clinical settings. An integrative synthesis of systematic review evidence is required to better understand the effects of these interventions. Our objective was to synthesize the systematic review evidence on the effect of interventions targeting the behaviours of physician opioid prescribers for CNCP among adults on patient and population health and prescriber behaviour. METHODS: We searched MEDLINE, Embase, and PsycInfo via Ovid; the Cochrane Database of Systematic Reviews; and Epistemonikos. We included systematic reviews that evaluate any type of intervention aimed at impacting opioid prescriber behaviour for adult CNCP in an outpatient setting. RESULTS: We identified three full texts for our review that contained 68 unique primary studies. The main interventions we evaluated were structured prescriber education (one review) and prescription drug monitoring programmes (PDMPs) (two reviews). Due to the paucity of data available, we could not determine with certainty that education interventions improved outcomes in deprescribing. There is some evidence that PDMPs decrease the number of adverse opioid-related events, increase communication among healthcare workers and patients, modify healthcare practitioners' approach towards their opioid prescribed patients, and offer more chances for education and counselling. CONCLUSIONS: Our overview explores the possibility of PDMPs as an opioid deprescribing intervention and highlights the need for more high-quality primary research on this topic.
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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 | 0.016 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".