Incidence and correlates of opioid-related psychiatric emergency care: A retrospective, multiyear cohort study
Bibliographic record
Abstract
BACKGROUND: In 2018, nearly 4,000 Canadian lives were claimed by the opioid epidemic. To date, only a few studies have reviewed shifts in emergency department (ED) utilization for opioid-related psychiatric presentations. AIMS: To describe the characteristics of patients seeking ED care for opioid-related psychiatric presentations and to identify demographic and clinical characteristics that were associated with psychiatric inpatient admission for such presentations. METHODS: Retrospective cohort study with multivariate logistic regression. FINDINGS: Over a 4-year period, 555 opioid-related presentations were recorded (50 percent female, mean age 40.0 years). Time trend analysis showed a nonsignificant increase in the number of visits by fiscal year. The most common reason for ED presentation relevant to opioids was opioid withdrawal (49 percent). Nearly 20 percent of all visits required psychiatric admission; predictors of psychiatric admission were arrival by ambulance (adjusted odds ratio (AOR) = 2.03), older age (AOR = 1.05), longer length of ED stay (AOR = 1.10), and more severe triage score (AOR = 0.4). Sex and referring service were not associated with disposition in the ED. Admissions were more likely for opioid intoxication and withdrawal. CONCLUSION: EDs are serving increasing numbers of patients in psychiatric crisis related to opioid-use. A decision support tool could be developed and validated in the future to provide reliable, clinically relevant information to providers and case managers relevant to opioid-related ED presentations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".