Incidence and Risk Factors of Intracranial Atherosclerotic Stroke: The Northern Manhattan Stroke Study
Bibliographic record
Abstract
BACKGROUND: To assess the prevalence of risk factors as determinants of intracranial atherosclerosis (IATH)-related stroke in a multi-ethnic community-based cohort. METHODS: The Northern Manhattan Stroke Study included a population-based incidence study and a nested case-control study. Incident cases of first ischemic stroke were 1:2 when matched to community controls by age, sex, and race/ethnicity. Vascular risk factors were assessed among controls and compared against the following stroke subtypes: IATH, extracranial atherosclerosis (EATH), and non-atherosclerotic (NATH: cardioembolic, lacunar, and cryptogenic). Conditional logistic-regression was used to determine the association between risk factors and stroke subtypes. RESULTS: The crude incidence of IATH was 8/100,000 per year and the relative incidence of IATH was higher than that of EATH in blacks (5.9 vs. 3.2/100,000 per year) and in Hispanics (5.0 vs. 1.7/100,000 per year). The IATH group had a higher prevalence of diabetes mellitus (DM; 67% IATH, 60% EATH, 48% NATH, and 23% controls; p < 0.05 IATH vs. control) and of metabolic syndrome (62% IATH, 40% EATH, 40% NATH, and 35% controls; p < 0.05 IATH vs. control). In multivariate analysis, DM conferred a higher risk for IATH versus NATH stroke (OR, 10.8; 95% CI, 2.0-57 vs. OR, 2.7; 95% CI, 1.9-3.9; p < 0.05) and much lower for EATH (OR, 6.2; 95% CI, 1.2-32). The metabolic syndrome conferred a higher risk for IATH stroke subtype (OR, 4.6; 95% CI, 1.1-18.7) when compared to EATH (OR, 2.3; CI, 0.6-9.1) and NATH (OR, 2.4; CI, 1.7-3.3). CONCLUSIONS: DM is a more important determinant for IATH-related stroke than EATH or NATH.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".