Impact of Dyslipidemia, Cardiovascular Disease, Smoking, and Stroke Type by Brain Imaging on Pneumonia Risk in ICU Stroke Patients: A Cross-Sectional Observational Study.
Bibliographic record
Abstract
Background: Stroke-associated pneumonia (SAP) is an associated condition after stroke and is strongly related to prolonged hospitalization and a high mortality rate. While comorbidities are often assumed to influence pneumonia risk, their role remains uncertain across stroke subtypes. Objective: To identify predictors of pneumonia in ischemic and hemorrhagic stroke patients, with emphasis on age, sex, and common vascular comorbidities. Methods: We analyzed 300 stroke patients who underwent a tertiary care hospitalization, including 237 ischemic and 63 hemorrhagic cases. Demographic and clinical variables, such as sex, hypertension, ischemic heart disease, smoking, and dyslipidemia, were assessed. Pneumonia occurrence was the primary outcome. Chi-square tests and binary logistic regression were used to identify independent predictors. Results: Pneumonia developed in 55% of ischemic stroke patients and 17.5% of those with hemorrhagic stroke. In ischemic stroke, age was the only independent predictor of pneumonia (OR: 1.01; 95% CI: 1.00-1.03; p = 0.042). Sex and comorbidities showed no significant associations. In hemorrhagic stroke, none of the evaluated variables predicted pneumonia. Conclusion: Post-stroke pneumonia was more common in ischemic than hemorrhagic stroke. Age was the sole independent predictor among ischemic patients, while traditionally assumed comorbidities were not significant. Preventive strategies should focus on older ischemic patients, incorporating systematic aspiration screening and targeted pulmonary care. A bigger prospective multicenter data evaluation is needed to validate and expand these findings.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".