Opioid stewardship after emergency laparoscopic general surgery
Bibliographic record
Abstract
BACKGROUND: Opioid administration in postoperative patients has contributed to the opioid crisis by increasing the load of opioids available in the community. Implementation of evidence-based practices is key to optimizing the use of opioids for acute pain control. This study aims to characterize the administration and prescribing practices after emergency laparoscopic general surgery procedures with the goal of identifying areas for improvement. METHODS: A retrospective chart review of 200 patients undergoing emergency laparoscopic appendectomies and cholecystectomies was conducted for a 2-year period at a single institution. Eligible patients were opioid-naïve adults admitted through the emergency department. Opioid administration and discharge prescriptions were converted to oral morphine equivalents (OME), and analyzed and compared with published literature and local guidelines. RESULTS: Opioid analgesia was provided as needed to 69% of patients in hospital with average dosing of 26.7 OME/day; comparatively, 99.5% of patients received prescriptions for opioids on discharge at an average dosing of 61.7 OME/day. The average dosing in the discharge prescriptions was not correlated with in-hospital needs (Pearson=-0.04; p=0.56); and higher narcotic doses were associated with combination opioid prescriptions compared with separate opioid prescriptions (73.8 (1.90) vs. 50.1 (1.90) OME/day; p<0.01). This difference was driven by the combination medication, Percocet. CONCLUSIONS: In the immediate postoperative period, most patients were managed in hospital with opioid analgesia dosages that fell within guidelines. Nearly all patients were provided with prescriptions for opioids on discharge, these prescriptions both exceeded local guidelines and were not correlated with in-hospital narcotic needs or pain scores. LEVEL OF EVIDENCE: Level 3 retrospective cohort study.
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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 | 0.009 |
| 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 | 0.001 |
| 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".