Opioid prescribing patterns following surgical interventions for benign prostatic hyperplasia
Bibliographic record
Abstract
INTRODUCTION: This study aimed to analyze opioid prescribing behavior following surgical interventions for benign prostatic hyperplasia (BPH), focusing on differences in postoperative opioid prescribing practices between patients who undergo surgical procedures in operative room (OR) settings vs. non-operative room (non-OR) settings. METHODS: Plus for Academics database, including men who underwent surgical interventions for BPH from 2015-2020. Propensity score analysis and inverse probability treatment weighting were employed to adjust for potential confounders. Primary outcomes included opioid receipt, cumulative days of opioid use, and morphine equivalent daily dose (MEDD). RESULTS: Among the included men (n=6022), those undergoing procedures in OR settings were more likely to receive opioid prescriptions postoperatively compared to those in non-OR settings (42.78% vs. 28.00%, p<0.001). While cumulative days and MEDD of opioid prescriptions did not significantly differ between groups, there was a statistically significant difference in the distribution of opioid receipt duration (p=0.0128). The adjusted model showed significantly higher odds of opioid prescription for men undergoing OR procedures (odds ratio 1.922, 95% confidence interval 1.690-2.185). CONCLUSIONS: Men undergoing BPH surgeries in OR settings were more likely to receive opioid prescriptions postoperatively, suggesting potential overprescription. Despite similar cumulative opioid use, differences in prescription rates indicate a need for improved postoperative pain management strategies, possibly using non-opioid alternatives. Future research should focus on optimizing pain control, characterizing actual opioid consumption, and considering patient-specific factors in surgical decision-making.
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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 | 0.002 |
| 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) | 0.002 | 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".