Differences in Opioid Prescribing Practices among Plastic Surgery Trainees in the United States and Canada
Bibliographic record
Abstract
BACKGROUND: Overprescribing following surgery is a known contributor to the opioid epidemic, increasing the risk of opioid abuse and diversion. Trainees are the primary prescribers of these medications at academic institutions, and little is known about the factors that influence their prescribing. The authors hypothesized that differences in health care funding and delivery would lead to disparities in opioid prescribing. Therefore, the authors sought to compare the prescribing practices of plastic surgery trainees in the United States and Canada. METHODS: A survey was administered to trainees at a sample of U.S. and Canadian institutions. The survey queried opioid-prescriber education, factors contributing to prescribing practices, and analgesic prescriptions written after eight procedures. Oral morphine equivalents were calculated for each procedure and compared between groups. RESULTS: One hundred sixty-two trainees completed the survey, yielding a response rate of 32 percent. Opioid-prescriber education was received by 25 percent of U.S. and 53 percent of Canadian trainees (p < 0.0001). Preoperative counseling was performed routinely by only 11 percent of U.S. and 14 percent of Canadian trainees. U.S. trainees prescribed significantly more oral morphine equivalents than Canadians for seven of eight procedures (p < 0.05). Residency training in the United States and junior training level independently predicted higher oral morphine equivalents prescribed (p < 0.05). CONCLUSIONS: U.S. trainees prescribed significantly more opioids than their Canadian counterparts for seven of eight procedures surveyed. Many trainees are missing a valuable opportunity to provide opioid counseling to patients. Standardizing trainee education may represent an opportunity to reduce overprescribing.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".