Analysis of STEMI and NSTEMI in a Community Cohort of Marijuana Users
Bibliographic record
Abstract
Background: Marijuana is the most commonly used illicit drug in the United States. Current research has yet to come to a consensus on its association with acute coronary syndrome (ACS). Herein, we aimed to analyze how marijuana use relates to acute ST elevation myocardial infarction (STEMI) and non-STEMI (NSTEMI). Methods: Using a retrospective study design, we gathered data from August 2014 to September 2020 on all percutaneous coronary interventions (PCIs) done at Brookdale University Hospital Medical Center (BUHMC) in patients with NSTEMI and STEMI. To compare marijuana users (MUs) with non-users, t -tests and Chi-square tests were used. A total of 195 patients were included, with mean age at presentation of 47 years old; 59 were females (30.3%) and 136 were males (69.7%). We identified 37 patients who were MUs. MUs were younger than non-users (P < 0.01), had higher rates of alcohol (P = 0.025), opiate (P = 0.004) and cigarette (P ? 0.001) use. On admission, MUs had lower creatinine (P = 0.031), blood urea nitrogen (BUN) (P = 0.031), pro-B-type natriuretic peptide (PBNP) (P = 0.052), BMI (P = 0.014) and lower right coronary artery (RCA) disease (P = 0.026). Results: After logistic regression analyses, results showed that the severity of coronary artery disease (CAD) and admission diagnosis of STEMI or NSTEMI were not found to be significantly related to marijuana use. Age, alcohol, cigarette, creatinine, BUN, PBNP, BMI and RCA disease were significantly related to marijuana use. There was a negative correlation between marijuana use and RCA disease (MUs = 29.7% vs. non-users = 50%, P = 0.026). There was no significant association with STEMI, NSTEMI or the severity of CAD. Conclusion: As daily cannabis use is on the rise, more researches are needed to further determine the effects of marijuana use on CAD. Cardiol Res. 2022;13(5):297-302 doi: https://doi.org/10.14740/cr1340
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".