Opioids use after uro-oncologic surgeries in time of opioid crisis
Bibliographic record
Abstract
INTRODUCTION: Recent literature emphasizes how overprescription and lack of guidelines contribute to wide variation in opioid prescribing practices and opioid-related harms. We conducted a prospective, observational study to evaluate opioid prescriptions among uro-oncologic patients discharged following elective in-patient surgery. METHODS: Patients who underwent four surgeries were included: open retropubic radical prostatectomy, robot-assisted radical prostatectomy, laparoscopic radical nephrectomy, and laparoscopic partial nephrectomy. The primary outcome was the dose of opioids used after discharge (in oral morphine equivalents [MEq]). Secondary outcomes included: opioid requirements for 80% of the patients, management of unused opioids, opioid use three months postoperative, opioid prescription refills, and guidance about opioid disposal. RESULTS: Sixty patients were included for analysis. Patients used a mean of 30 MEq (95% confidence interval 17.8-42.2) at home and 80% of the patients used 50 MEq or less. A mean of 40.4 MEq per patient was overprescribed. Fifty percent of the patients kept the remaining opioids at home, with only 20.0% returning them to their pharmacy. After three months, 5.0% of the patients were using opioids at least occasionally. Three patients needed a new opioid prescription. Forty percent reported having received information regarding management of unused opioids. CONCLUSIONS: We found 60% of opioids prescribed were unused, with half of our patients keeping these unused tablets at home. Our results suggest appropriate opioid prescription amounts needed for urological cancer surgery, with 80% of the patients using 50 MEq or less of morphine equivalents.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".