Effect of emergency department opioid prescribing on health outcomes
Bibliographic record
Abstract
BACKGROUND: The relation between emergency department opioid prescribing and subsequent harm is complex and poorly studied. We sought to quantify adverse outcomes, incremental risk, and rates of prolonged opioid use among emergency department patients receiving an opioid prescription and propensity-matched controls. METHODS: We used administrative data to sample all Alberta emergency department visits over 10 years, excluding patients with cancer, palliative care, or concurrent opioid use. Treated patients filled an opioid prescription within 72 hours after their index visit; untreated patients did not. We generated propensity scores to identify matched controls among untreated patients. The 1-year primary composite outcome included opioid-related emergency visits (e.g., overdoses), new opioid agonist therapy, all-cause hospital admission, or death. The secondary outcome was prolonged opioid use. RESULTS: After 13 028 575 eligible visits, 689 074 patients (5.3%) filled an opioid prescription. The mean age was 43.9 years, and 49.8% of patients were female. Most were high-acuity patients with traumatic, gastrointestinal-genitourinary, or musculoskeletal complaints. Patients who received opioids experienced 1.4% more primary outcome events (17.1% v. 15.7%), driven by all-cause hospital admissions (16.4% v. 15.1%; number needed to harm [NNH] = 53) and prolonged opioid use (4.5% v. 3.3%; NNH = 59). Opioid-related visits, new opioid agonist treatment, and mortality were unaffected. Incremental risk was low for patients with documented mental health conditions or substance use, and was highest for opioid-naive patients, older patients, and males. INTERPRETATION: Emergency department opioid prescriptions were associated with small increases in subsequent opioid prescription use and hospital admission, particularly in older and opioid-naive patients, and males; they were not associated with overdoses, new opioid agonist therapy, or mortality. Physicians should understand patient-specific incremental risks when prescribing opioids for acute pain.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".