Abstract WMP100: Topographical Distribution of Lacunes in Cerebral Amyloid Angiopathy and Deep Hypertensive Intracerebral Hemorrhage
Bibliographic record
Abstract
Background/Aims: To evaluate whether the burden of lacunes located at deep and lobar brain regions, would differ between intracerebral hemorrhage patients (ICH) with cerebral amyloid angiopathy (CAA) vs patients with strictly deep cerebral microbleeds (CMB) and ICH (Deep HTN-ICH). Methods: We defined lobar and deep lacunes similar to the topographic distribution used for ICH and microbleeds. We then compared their distribution between CAA and Deep HTN-ICH patients. The independent associations of lacune location (lobar vs deep) with diagnosis of CAA-ICH and Deep HTN-ICH were evaluated using multivariable models. The relationship between lobar and deep lacunes and WMH volume was evaluated using partial correlation analyses adjusting for age and by means of a validated visual scale. Results: In our cohort of 316 ICH patients, lobar lacunes were more commonly present in CAA (20.4% vs 5.7% in Deep HTN-ICH, p<0.001; see figure) while deep lacunes more frequent in Deep HTN-ICH patients (15.2% vs 2.1%, p<0.001; see figure). After correction for demographics, clinical and neuroimaging markers of SVD, lobar lacunes were associated with CAA diagnosis (p=0.023), while deep lacunes with Deep HTN-ICH (p<0.001). Lobar lacunes in 80% of the cases were at least in contact with WMH and after adjustment for age they were highly correlated to WMH volume ( r=0.52, p<0.001). Conclusions: Lobar lacunes are associated with CAA whereas deep lacunes are more frequent in patients with Strictly deep CMBs and ICH, they can thus be clinically useful in the appropriate context. Lobar lacunes seem to have a close relationship with WMH suggesting a possible common origin.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".