Evaluation of a quality improvement bundle aimed to reduce opioid prescriptions after Cesarean delivery: an interrupted time series study
Bibliographic record
Abstract
PURPOSE: To evaluate whether opioid prescriptions at discharge after Cesarean delivery decreased following implementation of a quality improvement bundle. METHODS: A quality improvement bundle was instituted at Mount Sinai Hospital in Toronto. Interventions included opioid prescribing instructions in resident orientation, nursing and patient education, and standard electronic prescriptions. We used an interrupted time series study design and included patients who had a Cesarean delivery six months pre intervention and six months post intervention. Primary outcome data (opioids prescribed at discharge in morphine milliequivalents [MME]), were aggregated (averaged) by calendar week and analyzed using interrupted time series. Secondary outcomes were assessed using bivariate methods and included opioid use for breakthrough pain in hospital, and amount of opioids prescribed by prescriber specialty and training level. RESULTS: We included 2,578 women in our analysis. Based on the segmented regression analysis, prescribed opioids decreased from 97.6 MME in 2018 to 35.8 MME in 2019 (difference in means, - 61.7; 95% confidence interval [CI], - 72.2 to - 51.3; P < 0.001), and this decrease was sustained over the study period. Post intervention, there were no visits to our postnatal assessment clinic for inadequate pain control. CONCLUSION: A quality improvement bundle was associated with a marked and sustained decrease in discharge prescriptions of opioids post Cesarean delivery at a large Canadian tertiary academic hospital.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".