Physician Characteristics Associated With Opioid Prescribing After Same-Day Breast Surgery in Ontario, Canada: A Population-Based Cohort Study
Bibliographic record
Abstract
Background and Objectives: Opioid overprescribing in patients undergoing breast surgery is a concern, as evidence suggests that minimal or no opioid is needed to manage pain. We sought to describe characteristics of opioid prescribers and determine associations between prescriber's characteristics and high opioid prescribing within 7 days of same-day breast surgery. Methods: high opioid prescribing defined as >75th percentile of the mean oral morphine equivalents (OME; milligrams). Prescriber characteristics including age, sex, specialty, years in practice, practice setting, and history of high (>75th percentile) opioid prescribing in the previous year were captured. Associations between prescriber characteristics and the primary outcome were estimated in modified Poisson regression models. Results: The final cohort contained 56,434 patients, 3469 unique prescribers, and 58,656 prescriptions. Over half (1971/3469; 57%) of prescribers wrote ≥1 prescription that was >75th percentile of mean OME of 180 mg, of which 50% were family practice physicians. Adjusted mean OMEs prescribed varied by specialty with family practice specialties prescribing the highest mean OME (614 ± 38 mg) compared to surgical specialties (general surgery [165 ± 9 mg], plastic surgery [198 ± 10 mg], surgical oncology [154 ± 14 mg]). Whereas 73% of first and 31% of second prescriptions were provided by general surgery physicians, family practice physicians provided 2% of first and 51% of second prescriptions. Prescriber characteristics associated with a higher likelihood of high current opioid prescribing were family practice (risk ratio [RR], 1.56; 95% confidence interval [CI], 1.35-1.79 compared to general surgery), larger community practice setting (RR, 1.34; 95% CI, 1.05-1.71 compared to urban), and a previous high opioid prescribing behavior (RR, 2.28; 95% CI, 2.06-2.52). Conclusions: While most studies examine surgeon opioid prescribing, our data suggest that other specialties contribute to opioid overprescribing in surgical patients and identify characteristics of physicians likely to overprescribe.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 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".