Effect of preoperative cannabis use on perioperative outcomes: a retrospective cohort study
Bibliographic record
Abstract
INTRODUCTION: The reported use of cannabis within surgical population is increasing. Cannabis use is potentially associated with increased harms and varied effects on pain control. These have important implications to perioperative care. METHODS: We conducted a retrospective cohort study comparing surgical patients reporting cannabis use preoperatively to control patients with no cannabis exposure, in a 1:2 ratio. To control for confounding, we used a propensity score-matched analysis to assess the adjusted association between cannabis use and study outcomes. Our primary outcome was a composite of (1) respiratory arrest or cardiac arrest, (2) intensive care admission, (3) stroke, (4) myocardial infarction and (5) mortality during this hospital stay. Secondarily, we assessed the effects on pain control, opioid usage, induction agent dose and nausea-vomiting. RESULTS: Between January 2018 and March 2019, we captured 1818 patients consisting of cannabis users (606) and controls (1212). For propensity score-matched analyses, 524 cannabis patients were compared with 1152 control patients. No difference in the incidence of composite outcome was observed (OR 1.06, 95% CI 0.23 to 3.98). Although a higher incidence of arrhythmias (2.7% vs 1.6%) and decreased incidence of nausea-vomiting needing treatment (9.6% vs 12.6%) was observed with cannabis users vs controls, results were not statistically significant. No significant differences were observed with other secondary outcomes. CONCLUSION: Our results do not demonstrate a convincing association between self-reported cannabis use and major surgical outcomes or pain management. Perioperative decisions should be made based on considerations of dose, duration, and indication.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".