Silent Brain Infarction; Risk Factors and Effect on Outcome of Acute Ischemic Stroke
Bibliographic record
Abstract
Background: Stroke is a major contributor to disability and the second a major contributor of death globally. Silent brain infarction is an example of a subclinical risk factor for stroke that, if identified, might lead to earlier and more effective preventative measures.The aim of the work: To detect risk factors of silent brain infarctions [SBI] and its effect on outcome of first ever acute ischemic stroke.Patients and Methods: In a prospective research, 76 cases diagnosed clinically and radiologically as first ever ischemic stroke and admitted to emergency department and stroke unit of Al-Azhar University hospitals were enlisted beginning in December 2022 to May 2023. They were categorized into two groups: patients with SBIs [38 patients] and patients without SBIs [38 patients].Results: We assessed 76 cases presented with first ever acute ischemic stroke with and without SBI. Age was significantly higher in cases with SBI contrasted with cases without SBI with male to female ratio 1: 1.5 without significant difference. There was a significant variance among both groups regarding HTN, DM, ischemic heart diseases, and the degree of stenosis at right, left carotid and vertebrobasilar arteries, the size of infarction and the assessment of cognitive function using Mental State Examination [MSE] and Montreal Cognitive Assessment Scale [MOCA] after 3 months. The Modified Rankin score after 3 months from onset was significantly different between both groups and all cases with moderate to severe and severe disability were having SBI. And there was a significant variance regarding the baseline & after one-week National Institute of Health stroke scale [NIHSS].Conclusion: Hypertension was identified as the most important risk factor of SBI. SBI affects cognitive function and affected patients have a higher probability of developing vascular dementia in addition to Sever long term disability and functional outcome.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".