Risks associated with opioid prescriptions for headache in the emergency department
Bibliographic record
Abstract
STUDY HYPOTHESIS: Use of opioids for treatment of headache in the emergency department (ED) is associated with an increased 1-year risk of opioid-related adverse events. OBJECTIVE: To assess the safety and efficacy of opioid prescribing for ED patients with headache. METHODS: We performed a multicenter observational cohort study using linked administrative data. All patients discharged from an ED in the province of Alberta, Canada with a headache diagnosis between 2010 and 2020 were included. Opioid-treated patients filled opioid prescriptions within 72 h of their ED visit, and were matched to untreated controls using propensity scores. The 1-year primary outcome was a composite of long-term prescription opioid use (LTU), opioid-related ED visit or hospitalization, or new opioid agonist therapy (OAT). Secondary outcomes included all-cause acute care utilization and 7-day ED return headache visits. RESULTS: Of 323,932 eligible headache visits, 5.7 % received opioids. Opioid-treated patients were comparable to controls on all baseline characteristics. The primary outcome occurred in 8.7 % of opioid-treated patients and 5.8 % of controls (aOR 1.65 [1.49-1.82]; NNH = 29). Opioid-treated patients had higher rates of LTU (7.7 % vs. 4.8 %), all-cause ED visit (20.8 % vs. 19.0 %), all-cause hospitalization (16.7 % vs. 14.8 %), and 7-day revisit (aOR = 1.61 [1.49-1.74]; NNH = 21) but did not experience more opioid-related ED visits or hospitalizations, or new OAT. Opioid prescription potency and duration were strong predictors of harm. CONCLUSION: Opioid prescriptions are associated with ED revisits, hospitalizations and LTU in headache patients, without improved efficacy. These findings support the growing notion that opioids are not indicated for ED headache management.
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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 | 0.004 |
| 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 | 0.000 |
| 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".