Variability in opioid prescribing practices among cardiac surgeons and trainees
Bibliographic record
Abstract
BACKGROUND AND AIM: The opioid epidemic has become a major public health crisis in recent years. Discharge opioid prescription following cardiac surgery has been associated with opioid use disorder; however, ideal practices remain unclear. Our aim was to examine current practices in discharge opioid prescription among cardiac surgeons and trainees. METHODS: A survey instrument with open- and closed-ended questions, developed through a 3-round Delphi method, was circulated to cardiac surgeons and trainees via the Canadian Society of Cardiac Surgeons. Survey questions focused on routine prescription practices including type, dosage and duration. Respondents were also asked about their perceptions of current education and guidelines surrounding opioid medication. RESULTS: Eighty-one percent of respondents reported prescribing opioids at discharge following routine sternotomy-based procedures, however, there remained significant variability in the type and dose of medication prescribed. The median (interquartile range) number of pills prescribed was 30 (20-30) with a median total dose of 135 (113-200) Morphine Milligram Equivalents. Informal teaching was the most commonly reported primary influence on prescribing habits and a lack of formal education regarding opioid prescription was associated with a higher number of pills prescribed. A majority of respondents (91%) felt that there would be value in establishing practice guidelines for opioid prescription following cardiac surgery. CONCLUSIONS: Significant variability exists with respect to routine opioid prescription at discharge following cardiac surgery. Education has come predominantly from informal sources and there is a desire for guidelines. Standardization in this area may have a role in combatting the opioid epidemic.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".