National Long-Term Trends in Postoperative Opioid Prescribing in Ambulatory Urology Procedures
Bibliographic record
Abstract
INTRODUCTION: With more than 60% of urological procedures performed in ambulatory settings, it is imperative to understand the current trends in postoperative narcotic prescriptions and their adherence to the guidelines. We studied postoperative opioid-prescribing patterns after selected common urology ambulatory procedures. METHODS: A retrospective cohort was derived from a 10% random sample of enrollees within the IQVIA PharMetrics Plus for Academics database from 2015 to 2021. Patient-level baseline characteristics were collected in the year preceding the index date. Descriptive and bivariate analyses were used to compare patient characteristics from opioid and nonopioid cohorts and those who utilized opioids ≤ 7 days and > 7 days postprocedurally. Trends of opioid and nonopioid use were also investigated and compared. RESULTS: Between 2015 to 2021, 17,817 patients underwent urological ambulatory procedures, of which the majority (90.9%) were endoscopic procedures. Of those, 4077 (22%) were prescribed opioids and 978 (5.4%) patients were given prescription nonopioid (ie, ketorolac) medication. From 2015 to 2021, there was an overall decrease in prescription of opioids from 32% to 19%. The acute fulfillment (within 7 days of the procedure) of opioids had notably declined; however, there is a slight increase in the fulfillment of opioids beyond 7 days. CONCLUSIONS: Within the 7-day postsurgical period after ambulatory procedures, narcotic prescribing habits among urologists are congruent with current initiatives to reduce narcotic use in the setting of the opioid pandemic. However, beyond the 7-day postsurgical period, further guidelines are needed to guide narcotic prescribing habits.
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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 | 0.002 |
| 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) | 0.001 | 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".