P3‐403: CLINICAL AND IMAGING CHARACTERISTICS OF NONHYPERTENSIVE CEREBRAL SMALL VESSEL DISEASE
Bibliographic record
Abstract
Small vessel disease (SVD) affects cognitive function in not only vascular cognitive impairment but also neurodegenerative dementia. Hypertension is one of the most important determinants of SVD. A significant number of patients develop SVD regardless of hypertension. However, it is not well known about the distribution, severity, and clinical impact of SVD in patients without hypertension. This study aimed to investigate the characteristics of non-hypertensive SVD. This is a single-center prospective observational study with nested case-control design. Using a prospective stroke registry, non-hypertension was defined as meeting all the following criteria: i) no history of hypertension prior to stroke, ii) no antihypertensive medication before admission, at discharge, and during outpatient care until one year from stroke onset, iii) mean systolic blood pressure <140 mmHg and diastolic blood pressure <90 mmHg from 4 days after hospitalization to discharge, and iv) no left ventricular hypertrophy in electrocardiography. Hypertension was defined as the case in which an antihypertensive drug was prescribed in a past medical history or was administered at discharge. The hypertensive controls were matched with non-hypertensive cases using age and sex as matching variables (1:1 exact match). SVD was defined according to STRIVE criteria. SVD lesions were registered on the Montreal Neurological Institute templates, and lesion frequency map was reconstructed using in-house MATLAB code. Characteristic patterns of SVD distribution explored by comparisons between cases and controls. A total of 704 subjects were included, and mean age was 68.9±10.7 years. In the hypertensive group, diabetes, hyperlipidemia, atrial fibrillation, coronary artery disease, and antithrombotic agents use was significantly higher than the non-hypertensive group. In the non-hypertensive group, white matter hyperintensities lesion map revealed that the anterior temporal and inferior frontal areas were more frequently involved, and cerebral microbleeds were more frequently observed in the occipital cortex. In the case of lacunar infarction, the non-hypertensive group had fewer lesions in brainstem. We will investigate the interrelationships between these image factors and cognitive functions.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".