Cerebral Microbleeds
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Cerebral microbleeds (CMBs) are areas of low signal intensity on gradient echo T2*-weighted magnetic resonance imaging (T2*MRI) corresponding to hemosiderin deposits in the perivascular space. Microangiopathy from atherosclerosis or amyloid angiopathy might lead to the formation of these lesions; therefore, there may be associations between CMBs and cardiovascular risk factors, APOE allele status, and brain morphology. We examined these relationships in the Framingham Study (FHS). METHODS: In 472 subjects from the FHS Offspring and Cohort, we related CMB status to age, sex, systolic blood pressure, total cholesterol and high-density lipoprotein cholesterol (HDL-C) levels, smoking, diabetes, total hemispheric brain volume, white matter hyperintensity volume (WMHV), and APOE allele status. RESULTS: Overall prevalence of CMBs was 4.7%, but CMBs were more prevalent with advanced age and male sex. Blood pressure, brain volume, and WMHV were related to CMBs in crude analysis but not after adjustment for age and sex. There were no significant relationships demonstrated between CMBs and APOE allele status, cholesterol, smoking, or diabetes. CONCLUSIONS: There is a low prevalence of CMBs in the community and a strong relationship with increasing age and male sex. We found no independent relationships with cardiovascular risk factors, APOE status, brain volumes, or WMH.
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.
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 | 0.000 |
| 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) | 0.005 | 0.001 |
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".