Variation in opioid filling after same-day breast surgery in Ontario, Canada: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Postoperative pain management practices in breast surgery are variable, with recent evidence that approaches for minimizing or sparing opioids can be successfully implemented. We describe opioid filling and predictors of higher doses in patients undergoing same-day breast surgery in Ontario, Canada. METHODS: In this retrospective population-based cohort study, we used linked administrative health data to identify patients aged 18 years or older who underwent same-day breast surgery from 2012 to 2020. We categorized procedure types by increasing invasiveness of surgery: partial, with or without axillary intervention (P ± axilla); total, with or without axillary intervention (T ± axilla); radical, with or without axillary intervention (R ± axilla); and bilateral. The primary outcome was filling an opioid prescription within 7 or fewer days after surgery. Secondary outcomes were total oral morphine equivalents (OMEs) filled (mg, median and interquartile range [IQR]) and filling more than 1 prescription within 7 or fewer days after surgery. We estimated associations (adjusted risk ratios [RRs] and 95% confidence intervals [CIs]) between study variables and outcomes in multivariable models. We used a random intercept for each unique prescriber to account for provider-level clustering. RESULTS: < 0.0001). Factors associated with filling more than 1 opioid prescription were age 30-59 years (v. age 18-29 yr), increased invasiveness (RR 1.98, 95% CI 1.70-2.30 bilateral v. P ± axilla), Charlson Comorbidity Index ≥ 2 versus 0-1 (RR 1.50, 95% CI 1.34-1.69) and malignancy (RR 1.39, 95% CI 1.26-1.53). INTERPRETATION: Most patients undergoing same-day breast surgery fill an opioid prescription within 7 days. Efforts are needed to identify patient groups where opioids may be successfully minimized or eliminated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".