Incidence and Risk Factors of Intracranial Atherosclerotic Stroke: The Northern Manhattan Stroke Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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 it