A Systematic Review of Behavioral Interventions to Decrease Opioid Prescribing After Surgery
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to summarize strategies to reduce postsurgical opioid prescribing at discharge. SUMMARY BACKGROUND DATA: Current practices for the prescription of opioids at discharge after surgery are highly variable and often excessive. We conducted a systematic review to identify behavioral interventions designed to improve these practices. METHODS: We searched MEDLINE, EMBASE, CINAHL, and PsycINFO until December 14, 2018 to identify studies of behavioral interventions designed to decrease opioid prescribing at discharge among adults undergoing surgery. Behavioral interventions were defined according to the Cochrane Effective Practice and Organisation of Care (EPOC) taxonomy. We assessed the risk of bias of included studies using criteria suggested by Cochrane EPOC and the Newcastle-Ottawa scale. RESULTS: Of 8048 citations that were screened, 24 studies were included in our review. Six types of behavioral interventions were identified: local consensus-based processes (18 studies), patient-mediated interventions (2 studies), clinical practice guidelines (1 study), educational meetings (1 study), interprofessional education (1 study), and clinician reminder (1 study). All but one study reported a statistically significant decrease in the amount of opioid prescribed at discharge after surgery, and only 2 studies reported evidence of increased pain intensity. Reductions in prescribed opioids ranged from 34.4 to 212.3 mg morphine equivalents. All studies were found to have medium-to-high risks of bias. CONCLUSIONS: We identified 6 types of behavioral strategies to decrease opioid prescription at discharge after surgery. Despite the risk of bias, almost all types of intervention seemed effective in reducing opioid prescriptions at discharge after surgery without negatively impacting pain control.
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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 | 0.014 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".