Opioid prescribing practices in breast oncologic surgery—A retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: In breast oncologic surgery, 75% of patients receive a postoperative opioid prescription at discharge, and 10%-20% will develop persistent opioid use. To inform future institutional guidelines, the objective of this study was to determine baseline opioid prescribing patterns in a single high-volume, referral-based breast center. We hypothesized that opioid prescribing practices varied between procedures and operating surgeons. METHODS: A retrospective analysis of all women undergoing breast cancer surgery between January and December 2019. Opioid prescriptions at discharge were converted to morphine milligram equivalents (MME). The primary outcome of interest was MME prescribed at discharge. Multiple linear regression was used to identify factors independently associated with MME prescribed. RESULTS: 392 patients met inclusion criteria; 68.3% underwent partial mastectomy. Median age was 61 (interquartile range [IQR] 51-70). Median MME prescribed at discharge was 112.5 (IQR 75-150); 83.9% of patients were prescribed co-analgesia. The prescriber was a trainee in 37.7% of cases. 15 patients (3.8%) required opioid renewal. On multivariate analysis, axillary procedure was associated with increased MME (ß = 17, 95% CI 5.5-28 and ß = 32, 95% CI 17-47, for sentinel node and axillary dissection, respectively). However, the factor with the greatest impact on MME was operating surgeon (ß = 72, 95% CI 58-87). Residents prescribed less MME compared to attending surgeons (ß = 11, 95% CI -22; -0.06). CONCLUSION: In a tertiary care center, the operating surgeon had the greatest influence on opioid prescribing practices, and trainees tended to prescribe less MME. These findings support the need for a standardized approach to optimize prescribing and reduce opioid-related harms after oncologic breast surgery.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".