A retrospective study comparing postoperative opioid prescribing practices in an academic medical centre
Bibliographic record
Abstract
Background: In the midst of the North American opioid crisis, identifying and intervening on drivers of high-risk opioid prescriptions is an important step towards reducing iatrogenic harm. Objectives: We aimed to identify factors associated with variations in high-risk opioid discharge prescriptions, following select surgical procedures, to guide future quality improvement initiatives. Methods: This retrospective cohort study analyzed 1322 patients who underwent select open pelvic and open abdominal surgeries between January 1 and December 31, 2017, in a tertiary health care centre in Montreal. Results: Patients who underwent open abdominal surgeries were prescribed significantly higher daily doses of morphine milligram equivalents (MME) (45 mg; interquartile range, 30-60), than patients who underwent either a caesarean delivery (20 mg, 20-20) or a hysterectomy (30 mg, 22-30). After adjustment for multiple potential confounders, abdominal surgery was associated with 4 times the odds of receiving more than 50 MME at hospital discharge compared with pelvic surgeries (odds ratio, 3.96; 95% confidence interval, 1.31-11.97). The availability of postoperative preprinted order sets with fixed high doses of opioids was also highly associated with the outcome. Conclusion: In our institution, some surgeries were more likely to receive high-risk opioid prescriptions at discharge. Efforts to optimize safer prescribing practices should address the creation and/or updating of preprinted order sets to reflect current best practice guidelines. This initiative could be overseen by hospital pharmacy and therapeutics committees.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".