Pain Medication Prescribing Patterns in Augmentation Mammoplasty
Bibliographic record
Abstract
Background: The rate of opioid prescribing after low-risk surgical procedures has increased over the past decade, and surgeons are responsible for prescribing approximately one-third of all opioid medications. There is additional supporting evidence that patients only consume about half of the opioids prescribed to them after outpatient plastic surgery. Currently, there is no literature to provide surgeons with reference ranges for how much opioid medication will adequately provide analgesia for patients after undergoing bilateral breast augmentation (BBA) surgery. Objective: To quantify the amount of opioid medication required to adequately control pain for patients after undergoing BBA and use these data to provide recommendations on opioid prescribing practices. Methods: Cross-sectional prospective data were obtained through a take-home medication and pain tracking questionnaire for 56 patients after they underwent either subpectoral or subglandular BBA. Patients documented their pain scores on a 0 to 10 analogue scale and documented the type and amount of pain medication they took for a 7-day period. Results: Our study demonstrated that patients in the subglandular BBA group required an average of either 25 ± 1.2 Tylenol #3 or 19.3 ± 2.3 Tramacet tablets, and the subpectoral group required 27.7 ± 1.7 Tylenol #3 or 25.6 ± 0.9 Tramacet tablets over a 7-day period. There was no statistically significant difference between the 2 surgical groups. Conclusion: We propose a reference range of medication required on average for patients undergoing BBA to obtain adequate pain control in the initial postoperative period that falls within the most recent Canadian guidelines for safe opioid prescribing practices.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".