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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".