Quantifying unused opioids following emergency and ambulatory care: A systematic review and meta‐analysis
Bibliographic record
Abstract
Objective To quantify unused opioids among adult and pediatric patients discharged from the emergency department (ED) or ambulatory care settings with a prescription for acute pain. Methods We searched MEDLINE, Embase, CINHAL, PsycINFO, the Cochrane Library, and the gray literature from inception to April 29, 2021. We included observational studies in which any patient with an acutely painful condition received a prescription for an opioid on discharge from an outpatient care setting, and unused opioids were quantified. Two reviewers screened records for eligibility, extracted data, and conducted the quality assessment. Where possible, we pooled data and otherwise described the results of studies narratively. Total unused prescriptions were synthesized using a weighted average. Random effects models were used, and heterogeneity was measured by the I2 statistic. Our primary outcome was the quantity of unused opioid medication available after receiving a prescription for acute pain. Secondary outcomes were the proportion of patients with unused opioids following a prescription, the proportion of patients using no opioids, morphine equivalents of unused opioids, and factors associated with leftover opioids. Results In this systematic review and meta-analysis of 9 studies in emergency and ambulatory care settings, 59.6% of prescribed opioids remained unused; pediatric patients had 69.3% of their prescriptions remaining, compared to 54.6% among adult patients. The highest proportion of unused opioids was found following dental extractions (82.6%). Conclusions and Relevance More than 50% of opioids remain unused following prescriptions for acute pain. Responsible prescribing must be accompanied by education on safer use, storage, and disposal.
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 | 0.021 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.044 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".