The Association of Cannabis Use After Discharge From Surgery With Opioid Consumption and Patient-reported Outcomes
Bibliographic record
Abstract
OBJECTIVE: To compare outcomes of patients using versus not using cannabis as a treatment for pain after discharge from surgery. BACKGROUND: Cannabis is increasingly available and is often taken by patients to relieve pain. However, it is unclear whether cannabis use for pain after surgery impacts opioid consumption and postoperative outcomes. METHODS: Using Michigan Surgical Quality Collaborative registry data at 69 hospitals, we analyzed a cohort of patients undergoing 16 procedure types between January 1, 2021, and October 31, 2021. The key exposure was cannabis use for pain after surgery. Outcomes included postdischarge opioid consumption (primary) and patient-reported outcomes of pain, satisfaction, quality of life, and regret to undergo surgery (secondary). RESULTS: Of 11,314 included patients (58% females, mean age: 55.1 years), 581 (5.1%) reported using cannabis to treat pain after surgery. In adjusted models, patients who used cannabis consumed an additional 1.0 (95% CI: 0.4-1.5) opioid pills after surgery. Patients who used cannabis were more likely to report moderate-to-severe surgical site pain at 1 week (adjusted odds ratio: 1.7, 95% CIL 1.4-2.1) and 1 month (adjusted odds ratio: 2.1, 95% CI: 1.7-2.7) after surgery. Patients who used cannabis were less likely to endorse high satisfaction (72.1% vs 82.6%), best quality of life (46.7% vs 63.0%), and no regret (87.6% vs 92.7%) (all P < 0.001). CONCLUSIONS: Patient-reported cannabis use, to treat postoperative pain, was associated with increased opioid consumption after discharge from surgery that was of clinically insignificant amounts, but worse pain and other postoperative patient-reported outcomes.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".