Postoperative opioid-prescribing patterns among surgeons and residents at universityaffiliated hospitals: a survey study
Bibliographic record
Abstract
Background: Overprescribing of opioids to patients following surgery is a public health concern, as unused pills may be diverted and contribute to opioid misuse and dependence. The objectives of this study were to determine current opioid-prescribing patterns for common surgical procedures, factors that affect surgeons’ prescribing behaviour and their perceived ability to manage patients with opioid use disorder. Methods: Survey participants included all consultant and trainee surgeons at the University of Toronto. The survey, which was administered electronically, included 52 multiple-choice, rank-order and open-text questions eliciting information on current prescribing patterns, prescribing of adjunct pain medications, and education and other factors related to opioid prescribing. Staff surgeons were also asked about how they manage patients with a suspected opioid issue. Results: Eighty surgical trainees and 40 staff surgeons responded to the survey (response rate 32%). Five staff surgeons (12%) felt adequately educated to prescribe pain medications (including opioids) at discharge. Staff surgeons prescribed Tylenol 3 more frequently than other opioids. Twenty (51%) of 39 staff surgeons reported that they sought further help for their patients when an opioid use disorder was suspected. Conclusion: Our results support existing studies showing a large degree of variability in postoperative opioid prescribing. Institutional guidelines have been shown to be effective in curbing excessive opioid prescribing without increasing unnecessary emergency department visits for uncontrolled pain. Thus, there is an opportunity to develop institutional guidelines to educate surgical teams in the prescribing of opioids and about services available for patients with a substance use disorder.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".