Optimizing opioid prescriptions after laparoscopic appendectomy and cholecystectomy
Bibliographic record
Abstract
Background: There has been an increase in opioid usage and opioid-related deaths. Opioids prescribed to surgical patients have similarly increased. The aim of this study was to assess opioid consumption in patients undergoing laparoscopic appendectomy (LA) and laparoscopic cholecystectomy (LC) and to determine whether a standardized prescription could affect opioid consumption without affecting patient satisfaction. Methods: Patients undergoing LA or LC were recruited prospectively during 2 time periods (April to June 2017 and November 2017 to January 2018). In the first phase, surgeons continued their usual postoperative analgesia prescribing patterns. In the second phase, a standardized prescription was implemented. Patients were contacted by telephone and a questionnaire was completed for both phases of the study. The primary outcome was the quantity of opioids prescribed and consumed. Results: In the first phase, 166 patients who underwent LC or LA were recruited. The median number of prescribed opioid tablets was 20 and the median number consumed was 2. Ninety-five percent of patients reported satisfaction with their analgesia. Based on these results, a standardized prescription for multimodal analgesia was implemented for the second phase, consisting of 10 opioid tablets. In the second phase, 129 patients who underwent LA or LC were recruited. There was a significant decrease in the median number of opioid pills filled (10) and consumed (0), with no difference in reported satisfaction with analgesia. Conclusion: Patients are prescribed an excess of opioids after LA or LC. Implementation of a standardized prescription based on a quality improvement intervention was effective at decreasing the number of opioids prescribed and consumed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".