Cerebrovascular Risk Profiles in a Saudi Arabian Cohort of Young Stroke Patients
Bibliographic record
Abstract
Background: The constantly increasing incidence of stroke in younger individuals substantiates an urgent need for research to elucidate underlying risk factors and etiologies. Heretofore, the vast majority of studies on stroke in the young have been carried out in European and North American regions. We aimed to characterize cerebrovascular risk profiles in a Saudi Arabic cohort of consecutive young stroke patients. Methods: We retrospectively analyzed data from consecutive ischemic stroke patients aged 15 to 49 years who underwent detailed cardiocerebrovascular evaluation at a tertiary stroke care center in Makkah, Saudi Arabia. Distributions of risk factors and stroke etiologies were assessed in the entire cohort and in two strata of very young (15–40 years) and young to middle-aged patients (41–49) to account for variability in suggested age cutoffs. Results: In the entire cohort [n = 63, ages 44 (34–47) median, interquartile range], dyslipidemia (71.4%) and small vessel occlusion (31.7%) displayed highest prevalence followed by diabetes (52.4%) and cardioembolism (19%). In very young patients, cardioembolism was the most prevalent etiology (27.3%). Risk profiles were similar between both age strata except for a higher prevalence of diabetes among the older cohort (31.8 vs. 63.4%, p = 0.01). Logistic regression identified diabetes as strongest predictor for association to the older strata (odds ratio = 4.2, 95% confidence interval = 1.2–14.1, p = 0.02). Conclusion: Cerebrovascular risk profiles and stroke etiologies in our cohort of young stroke patients differ from those of previous cohorts, suggesting the need for tailored prevention strategies that take into account local epidemiological data on cerebrovascular health.
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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) | 0.001 | 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".