Excess Opioid Medication and Variation in Prescribing Patterns Following Common Breast Plastic Surgeries
Bibliographic record
Abstract
Purpose: Excess opioid prescribing has societal impacts including addiction, dependence, and misuse. This study aims to investigate prescribing patterns and self-reported patient experiences with opioid use, pain control, and disposal of unused medication following common breast surgeries. Methods: A total of 46 patients undergoing 5 breast procedures were identified during a predefined 14-week period. All procedures were carried out at a single tertiary care hospital by 9 plastic surgeons. Provincial narcotic monitoring program provided linked prescription information for identified patients. All patients were invited to participate in a telephone interview regarding postoperative opioid use. Results: A total of 41.6% of patients received and filled an opioid prescription following a breast procedure. Hydromorphone was the most commonly prescribed narcotic. The average number of opioid tablets dispensed following breast procedures was 31.9. Four percent of breast patients required an opioid refill. A total of 75% of breast patients used at least 1 over-the-counter analgesic, most commonly acetaminophen alone. Average self-reported pain score and total pain period were not significantly different between those using opioids and those not. A total of 6.7% and 23.1% of patients report returning excess narcotics to a pharmacy, while the majority report still having or self-disposing of excess tablets. Conclusions: Opioids are prescribed in excess for the breast procedures we analyzed. The majority of unused opioids were noted to still be at home or disposed of inappropriately. This suggests a role for reviewing opioid-prescribing patterns for common plastic surgery procedures to reduce the burden of the ongoing opioid epidemic.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".