Interventions for physician prescribers of opioids for chronic non-cancer pain: protocol for an overview of systematic reviews
Bibliographic record
Abstract
INTRODUCTION: Interventions targeting behaviours of physician prescribers of opioids for chronic non-cancer pain have been introduced to combat the opioid crisis. Systematic reviews have evaluated effects of specific interventions (eg, prescriber education, prescription drug monitoring programmes) on patient and population health outcomes and prescriber behaviour. Integration of findings across intervention types is needed to better understand the effects of prescriber-targeted interventions. METHODS AND ANALYSIS: We will conduct an overview of systematic reviews. Eligible systematic reviews will include primary studies that evaluated any intervention targeting the behaviours of physician prescribers of opioids for chronic non-cancer pain in an outpatient or mixed setting, compared with no intervention, usual practice or another active or control intervention. Eligible outcomes will pertain to the intervention effect on patient and population health or opioid prescribing behaviour. We will search MEDLINE, Embase and PsycInfo via Ovid; the Cochrane Database of Systematic Reviews and Epistemonikos from inception. We will also hand search reference lists for additional publications. Screening and data extraction will be conducted independently by two reviewers, with disagreements resolved by consensus or consultation with a third reviewer. The risk of bias of included systematic reviews will be assessed in duplicate by two reviewers using the Risk of Bias in Systematic Reviews tool. Results will be synthesised narratively by intervention type and grouped by outcome. To assist with result interpretation, outcomes will be labelled as intended or unintended according to intervention objectives, and as positive, negative, evidence of no effect or inconclusive evidence according to effect on the population (for patient and population health outcomes) or intervention objectives (for prescriber outcomes). ETHICS AND DISSEMINATION: As the proposed study will use published data, ethics approval is not required. Dissemination of results will be achieved through publication of a manuscript in a peer-reviewed journal and conference presentations. PROSPERO REGISTRATION NUMBER: CRD42020156815.
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 | 0.071 | 0.096 |
| Meta-epidemiology (narrow) | 0.009 | 0.008 |
| Meta-epidemiology (broad) | 0.021 | 0.024 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.089 | 0.015 |
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".