Abstract TP324: Macrobleeds and Microbleeds: A Vascular Risk Factor Microangiopathy
Bibliographic record
Abstract
Background: The predominant type of cerebral small vessel disease (SVD) and clinical outcomes of patients who present with a combination of lobar and deep intracerebral hemorrhage (ICH)/microbleed (MB) locations (Mixed-ICH, see figure) is unknown. Methods: Out of 391 consecutive ICH, 75 (19%) had Mixed-ICH, and their demographics, clinical/laboratory features, and SVD neuroimaging markers were compared to 191 probable Cerebral Amyloid Angiopathy (CAA-ICH) and 125 strictly deep-MB and ICH (Deep-ICH) patients. ICH-recurrence on follow up was also analyzed. Results: Mixed-ICH patients had a higher prevalence of hypertension, diabetes, left ventricular hypertrophy rates, higher creatinine values, as well as more prevalent lacunes and basal ganglia (BG) enlarged perivascular spaces (EPVS) than CAA-ICH (all p<0.05). When compared to Deep-ICH, Mixed-ICH patients were older, had higher WMH volumes and MB count, and more prevalent lacunes and centrum semiovale EPVS (all p<0.05). In multivariable models, Mixed-ICH diagnosis was associated with higher creatinine, more lacunes and BG EPVS, than CAA-ICH (all p<0.05). When compared to Deep-ICH, Mixed-ICH patients were older and had more lacunes and MBs in multivariable models (all p<0.05). Annual risk of ICH-recurrence was 5.1% for Mixed-ICH, higher compared to Strictly Deep-ICH but lower than CAA-ICH (1.6% and 10.4%, respectively). Conclusions: Mixed-ICH, commonly seen when MRI obtained during etiologic workup, appears to be mostly driven by vascular risk factors similar to Strictly Deep-ICH, but demonstrates more severe parenchymal damage and higher ICH-recurrence risk.
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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 | 0.000 | 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) | 0.006 | 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".