Abstract TP228: Association of Dependent Cannabis Use in Patients With Chronic Liver Disease With Stroke Hospitalizations and Subsequent Mortality: A Nationwide Analysis
Bibliographic record
Abstract
Introduction: Chronic liver disease (CLD) predisposes to systemic manifestations like thrombocytopenia and coagulopathy that can trigger stroke onset. Cannabis, due to its anti-inflammatory effects, could have a potential benefit for CLD patients. We aimed to study the impact of cannabis use disorder (CUD) or dependent use on stroke risk in CLD, considering recent American and Canadian surveys reporting 85% overlap between medicinal and recreational use. Methods: The National Inpatient Sample, 2019, was queried to identify adult stroke hospitalizations in patients with CLD. Two cohorts were created based on the presence of CUD. The primary outcomes were the rates and odds of stroke or acute ischemic stroke (AIS), with subsequent mortality in CUD vs. non-CUD cohorts. Secondary outcomes were resource utilization. Multivariable regression was controlled for patient-hospital-level factors and relevant comorbidities. Results: The CUD+ cohort had a lower rate of both overall stroke hospitalization (1.2% vs. 2.0%) and AIS hospitalization (0.8% vs. 1.2%) as compared to the CUD- cohort (p<0.001). The median age of admissions for stroke and CLD with CUD was 58 years, whereas it was 64 years in the non-CUD cohort. Traditional CVD risk burden was lower, but uncomplicated hypertension, PVD, prior MI, and drug abuse were more prevalent in the CUD+ cohort. Patients with CLD and CUD showed lower odds of any stroke (OR: 0.61, 95% CI: 0.51-0.73; p <0.001) and AIS (OR: 0.68, 95% CI: 0.55-0.85; p = 0.001) vs. the CUD- cohort. When adjusted for confounders, the odds of overall stroke risk were still lower (OR: 0.81; 95% CI: 0.66-0.98; p = 0.029) among the CUD+ cohort. However, adjusted odds for AIS showed no statistical significance (OR: 0.94; 95% CI: 0.74-1.19; p = 0.594). The all-cause mortality in the CUD+ cohort was lower compared to the CUD- cohort: 11.0% vs. 14.4%, p= 0.017, with shorter hospital stays and frequent routine discharges. Conclusion: This nationwide study revealed significantly reduced odds [20%] of stroke hospitalization in adults with CLD using cannabis on a regular, habitual basis. However, more prospective controlled studies are required to evaluate its impact on cardio-cerebrovascular risk with clarity on dose, duration, and mode of CUD in the long term.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".