Smoking Paradox in Patients Hospitalized With Coronary Artery Disease or Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND: Smoking is a potent risk factor for coronary artery disease (CAD) and acute ischemic stroke (AIS), but there are numerous reports of lower in-hospital mortality among smokers versus nonsmokers hospitalized for these events. METHODS AND RESULTS: We analyzed all consecutive patients hospitalized with a first index CAD (n=158 054) or AIS (n=899 295) event in Get With The Guidelines from 2002 to 2012; 20.4% of AIS and 30.4% of patients with CAD were past-year smokers. Multivariable models and age-stratified analyses were used to estimate the adjusted odds ratio of in-hospital mortality in smokers versus nonsmokers. Smokers were younger, more often male, with fewer vascular risk factors, and were more likely to be admitted to hospitals that were large, academic, or in the South. In-hospital mortality was significantly lower among smokers in both CAD (2.7% versus 5.2%; P<0.0001) and AIS (3.5% versus 5.8%; P<0.0001). The difference between unadjusted and adjusted odds ratios for smoking (0.57 versus 0.86 in CAD; 0.56 versus 0.86 in AIS) indicates the presence of substantial confounding by age and other covariates, but a significant association of past-year smoking remained. CONCLUSIONS: Among patients hospitalized with CAD and AIS, smoking is a risk factor for early age of onset, even among those with few vascular risk factors. The persistent association with lower in-hospital mortality after adjusted and stratified analyses probably represents residual unmeasured confounding, although a biological effect of smoking cannot be excluded. Further clinical and prospective population-based studies are needed to explore variables that contribute to outcomes in these patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".