Surgical Opioid Stewardship: From the Epidemic, Through the Pandemic, and Beyond
Bibliographic record
Abstract
BACKGROUND: In response to the opioid epidemic, prescribing guidelines and statewide surgical opioid management programs were initiated in 2018 to 2019. This analysis aimed to document the sustainability of a regional opioid stewardship consortium through the pandemic and beyond. STUDY DESIGN: From September 2019 to August 2023, 15 NSQIP hospitals in 2 states gathered opioid-specific variables on patients undergoing 12 procedures. Operations were performed by 7 specialties including general surgery, gynecology, orthopaedic surgery, neurosurgery, surgical oncology, urology, and vascular surgery. Data were gathered on preoperative opioid use, intraoperative opioids and blocks, opioid avoidance strategies, and discharge opioid prescriptions. Cochran-Armitage trend and Wald tests were used to assess temporal effects with p values <0.001 considered significant. RESULTS: Among 18,325 patients who underwent surgery, 54% were women, with a median age of 60 years. The percentage of patients taking opioids preoperatively decreased significantly (p < 0.001) in the overall cohort from 17% to 10%. The use of intraoperative blocks also increased significantly (p < 0.001) among all patients from 21% to 27%. The use of opioid avoidance strategies was maintained in the majority of the patients (68% to 70%), and the percentage of postoperative opioid prescriptions on discharge remained the same (81%) during the 4-year study period. CONCLUSIONS: From 2019 to 2023, the percentage of patients taking opioids preoperatively decreased significantly, and the use of intraoperative blocks also increased significantly. The Pennsylvania-New Jersey Surgical Opioid Stewardship program played a key role in combatting the opioid crisis during the 4 years of data collection, adapting effectively to the challenges posed by the COVID-19 pandemic.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".