Patterns of opioid prescribing to opioid‐naive patients after surgical and emergency care: A population‐based cross‐sectional study using linked administrative databases in Nova Scotia (2017–2019)
Bibliographic record
Abstract
INTRODUCTION: To describe opioid prescribing patterns for opioid-naive patients who filled prescriptions after surgical or emergency care. METHODS: We conducted a population-based, cross-sectional study of opioid-naive adults who filled opioid prescriptions within 14 days of receiving surgical or emergency care in Nova Scotia, Canada. Using linked administrative databases, we estimated the prevalence of opioid prescriptions with >7 days' supply, ≥90 morphine milligram equivalents (MME)/day or long-acting opioids. We assessed the association of care setting and specialty with these outcomes. RESULTS: Among 124,515 patients, 36,716 (29.5%) were opioid-naive. The median opioid supply duration was 3 days (IQR 2-5), the median dose was 50 MME/day (IQR 30-75). Prescriptions for >7 days, ≥90 MME/day or involving long-acting opioids were filled by 10.9%, 20.2% and 0.7% of the patients, respectively. Hydromorphone (50%) and codeine (26.4%) were the most filled opioids. The emergency care setting had double the odds of filling >7 days' supply (OR 2.13, 95% CI 1.99-2.28), and 69% lower chance of filling ≥90 MME/day (OR 0.31, 95% CI 0.29-0.33) than surgical care. In the surgical care setting, there was significant variation across medical specialties. Otolaryngology was associated with a higher chance of prescribing >7 days' opioid supply than general surgery (OR 4.89, 95% CI 3.86-6.20). Orthopaedic surgery had a higher likelihood of ≥90 MME/day prescriptions (OR 2.92, 95% CI 2.58-3.30) than general surgery. DISCUSSION AND CONCLUSIONS: Opioid prescribing patterns vary significantly by setting and specialty in Nova Scotia, Canada. Our results emphasise the need for tailored guidelines that consider clinical context and specialty to enhance patient safety and reduce opioid misuse risk.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".