Opioid Overdose in the Hospital Setting: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Our objective was to determine the percentage of opioid overdose events among medical and surgical inpatient admissions, and to identify risk factors associated with these events. METHODS: We searched PubMed and CINAHL databases from inception through July 30, 2017 and identified additional studies from reference lists and other reviews. Articles were included if they reported original research on the rate of opioid overdoses or opioid-related adverse events, and the adverse events occurred in a general medical hospital during an inpatient stay. We extracted information on study population, design, results, and risk for bias using the Newcastle-Ottawa Quality Assessment Scale. We performed this review in accordance with recently suggested standards and report our findings as per the Meta-Analyses and Systematic Reviews of Observational Studies guidelines. RESULTS: Thirteen studies met our eligibility criteria. The percentage of opioid overdoses ranged from 0.06% to 2.50% of hospitalizations. The majority of studies used only 1 method of event detection. Risk factors for overdose included older age, infancy, medical comorbidity, substance use disorder diagnosis, combining opioids with other sedatives, and admission to hospitals with higher opioid-prescribing rates. CONCLUSIONS: Opioid overdose in the inpatient setting is a serious preventable harm and is likely underestimated in much of the current literature. Improved detection methods are needed to more accurately measure the rate of inpatient opioid overdose. Refined estimates of opioid overdose should be used to drive safety and quality improvement initiatives in hospitals.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".