Prevalence and Risk Factors of Cerebral Microbleeds among Egyptian Patients with Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND: Cerebral microbleeds (CMBs) are markers of underlying hemorrhage-prone cerebral small vessel disease detected on MRI. They are associated with a heightened risk of stroke and cognitive decline. The prevalence of CMBs among Egyptian patients with ischemic stroke is not well studied. Our aim was to detect the prevalence of CMBs and associated risk factors among Egyptian patients with ischemic stroke. METHODS: A prospective, cross-sectional, single-center study of consecutive patients with ischemic stroke. Patients were recruited between January 2021 and January 2022 at the Assiut University Hospital in the south of Egypt. Patients with known bleeding diathesis were excluded. All participants underwent full neurological assessment, urgent laboratory investigations, and MRI with T2* sequence. RESULTS: The study included 404 patients, 191 (47.3%) of them were females. The mean age of the study population was 61 ± 1 years, and the mean NIHSS on admission was 12 ± 5. The prevalence of CMB was 26.5%, of whom 6.5% were young adults (age ≤45 years). CMBs were detected in 34.6% of patients with stroke caused by large artery atherosclerosis, 28.0% with small vessel disease stroke subtype, 25.2% with stroke of undetermined cause, and in 12.1% with cardioembolic stroke. History of AF, hypertension, dyslipidemia, Fazekas score >2, dual antiplatelet use, combined antiplatelet with anticoagulant treatment, and thrombolytic therapy remained independently associated with CMBs following multivariable regression analyses. CONCLUSION: The high number of identified CMBs needs to inform subsequent therapeutic management of these patients. We are unable to determine whether the association between CMBs and antithrombotic use is a causal relationship or rather confounded by indication for these treatments in our observational study. To understand more about the underlying cause of this finding, more studies are needed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".