Optimizing opioid prescribing practices after minimally invasive lung resection through a quality-improvement intervention
Bibliographic record
Abstract
BACKGROUND: In their 2019 guideline on the prescribing and management of opioids after elective ambulatory thoracic surgery, the Canadian Association of Thoracic Surgeons (CATS) recommended 120 morphine milligram equivalents (MME) after minimally invasive (video-assisted thoracoscopic surgery [VATS]) lung resection. We conducted a quality-improvement project to optimize opioid prescribing after VATS lung resection. METHODS: We assessed baseline prescribing practices for opioid-naive patients. Using a mixed-methods approach, we selected 2 quality-improvement interventions: formal incorporation of the CATS guideline into our postoperative care pathway, and development of a patient information handout regarding opioids. The intervention was initiated on Oct. 1, 2020, and was formally implemented on Dec. 1, 2020. The outcome measure was average MME of discharge opioid prescriptions, the process measure was proportion of discharge prescriptions exceeding the recommended dosage, and the balancing measure was opioid prescription refills. We analyzed the data using control charts, and compared all measures between the pre-intervention (12 mo before) and postintervention (12 mo after) groups. RESULTS: < 0.001). Control charts showed special cause variation corresponding with the intervention, and system stability existed after the intervention. There was no statistically significant difference in the proportion or dosage of opioid prescription refills after the intervention. CONCLUSION: After implementation of the CATS opioid guideline, there was a significant reduction in opioids prescribed at discharge and no increase in opioid prescription refills. Control charts are a valuable resource for monitoring outcomes on an ongoing basis and for assessing the effects of an intervention.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".