Patient characteristics associated with being offered take home naloxone in a busy, urban emergency department: a retrospective chart review
Bibliographic record
Abstract
BACKGROUND: Overdose deaths can be prevented by distributing take home naloxone (THN) kits. The emergency department (ED) is an opportune setting for overdose prevention, as people who use opioids frequently present for emergency care, and those who have overdosed are at high risk for future overdose death. We evaluated the implementation of an ED-based THN program by measuring the extent to which THN was offered to patients presenting with opioid overdose. We analyzed whether some patients were less likely to be offered THN than others, to identify areas for program improvement. METHODS: We retrospectively reviewed medical records from all ED visits between April 2016 and May 2017 with a primary diagnosis of opioid overdose at a large, urban tertiary hospital located in Alberta, Canada. A wide array of patient data was collected, including demographics, opioid intoxicants, prescription history, overdose severity, and whether a naloxone kit was offered and accepted. Multivariable analyses were used to identify patient characteristics and situational variables associated with being offered THN. RESULTS: Among the 342 ED visits for opioid overdose, THN was offered in 49% (n = 168) of cases. Patients were more likely to be offered THN if they had been found unconscious (Adjusted Odds Ratio 3.70; 95% Confidence Interval [1.63, 8.37]), or if they had smoked or injected an illegal opioid (AOR 6.05 [2.15,17.0] and AOR 3.78 [1.32,10.9], respectively). In contrast, patients were less likely to be offered THN if they had a current prescription for opioids (AOR 0.41 [0.19, 0.88]), if they were admitted to the hospital (AOR 0.46 [0.22,0.97], or if they unexpectedly left the ED without treatment or before completing treatment (AOR 0.16 [0.22, 0.97). CONCLUSIONS: In this real-world evaluation of an ED-based THN program, we observed that only half of patients with opioid overdose were offered THN. ED staff readily identify patients who use illegal opioids or experience a severe overdose as potentially benefitting from THN, but may miss others at high risk for future overdose. We recommend that hospital EDs provide additional guidance to staff to ensure that all eligible patients at risk of overdose have access to THN.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".