Sex-related differences in opioid administration in the emergency department: a population-based study
Bibliographic record
Abstract
BACKGROUND: Sex differences in pain experience and expression may influence ED pain management. Our objective was to evaluate the effect of sex on ED opioid administration. METHODS: We conducted a multicentre population-based observational cohort study using administrative data from Calgary's four EDs between 2017 and 2018. Eligible patients had a presenting complaint belonging to one of nine pain categories or an arrival pain score >3. We performed multivariable analyses to identify predictors of opioid administration and stratified analyses by age, pain severity and pain category. RESULTS: We studied 119 510 patients (mean age 47.4 years; 55.4% female). Opioid administration rates were similar for men and women. After adjusting for age, hospital site, pain category, ED length of stay and pain severity, male sex was not a predictor of opioid treatment (adjusted OR (aOR)=0.93; 95% CI 0.85 to 1.02). However, men were more likely to receive opioids in the categories of trauma (aOR=1.58, 95% CI 1.40 to 1.78), flank pain (aOR=1.24, 95% CI 1.11 to 1.38), headache (aOR=1.18, 95% CI 1.03 to 1.34) and abdominal pain (aOR=1.11, 95% CI 1.08 to 1.18). Pain category appears to be a strong determinant of opioid administration, especially back pain (aOR=6.56, 95% CI 5.99 to 7.19) and flank pain (aOR=6.04, 95% CI 5.48 to 6.65). There was significant variability in opioid provision by ED site (aOR 0.76 to 1.24). CONCLUSIONS: This population-based study demonstrated high variability in opioid use across different settings. Overall, men and women had similar likelihood of receiving opioids; however men with trauma, flank pain, headache and abdominal pain were much more likely to receive opioids. ED physicians should self-examine their analgesic practices with respect to possible sex biases, and departments should introduce evidence-based, indication-specific analgesic protocols to reduce practice variability and optimise opioid analgesia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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 teacher head, 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".