Standardization of Outpatient Procedure (STOP) Narcotics: A Prospective Non-Inferiority Study to Reduce Opioid Use in Outpatient General Surgical Procedures
Bibliographic record
Abstract
BACKGROUND: There has been a dramatic rise in opioid abuse, and diversion of excess, unused prescriptions is a major contributor. We assess the impact of implementing a new standardized pain care bundle to reduce postoperative opioids in outpatient general surgical procedures. STUDY DESIGN: This study was designed to demonstrate non-inferiority for the primary end point: patient-reported average pain in the first 7 postoperative days. We prospectively evaluated 224 patients who underwent laparoscopic cholecystectomy or open hernia repair (inguinal, umbilical) pre-intervention to 192 patients post-intervention. We implemented a multimodal intra- and postoperative analgesic bundle, including promoting co-analgesia, opioid-reduced prescriptions, and patient education designed to clarify patient expectations. Patients completed a brief pain inventory at their first postoperative visit. Groups were compared using chi-square test, Mann-Whitney U test, and independent samples t-test, where appropriate. RESULTS: No difference was seen in average postoperative pain scores in the pre- vs post-intervention groups (2.3 vs 2.1 of 10; p = 0.12). The reported quality of pain control improved post-intervention (good/very good pain control in 69% vs 85%; p < 0.001). The median total morphine equivalents for prescriptions filled in the post-intervention group were significantly less (100; interquartile range 75 to 116 pre-intervention vs 50; interquartile range 50 to 50 post-intervention; p < 0.001). Only 78 of 172 (45%) patients filled their opioid prescription in the post-intervention group (p < 0.001), with no significant difference in prescription renewals (3.5% pre-intervention vs 2.6% post-intervention; p = 0.62). CONCLUSIONS: For outpatient open hernia repair and cholecystectomy, a standardized pain care bundle decreased opioid prescribing significantly and frequently eliminated opioid use, and adequately treating postoperative pain and improving patient satisfaction.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".