Selective Vulnerability of Cortical Border Zone to Microembolic Infarct
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Endovascular procedures, including atrial fibrillation transcatheter ablation, may cause microembolization of brain arteries. Microemboli often cause small sized and clinically silent cerebral ischemias (SCI). These lesions are clearly visible on early postoperative magnetic resonance diffusion-weighted images. We analyzed SCI distribution in a population of patients submitted to atrial fibrillation transcatheter ablation. METHODS: Seventy-eight of 927 consecutive patients submitted to atrial fibrillation transcatheter ablation were found positive for acute SCI on a postoperative magnetic resonance. SCI were identified and marked, and their coordinates were transformed from native space into the International Consortium for Brain Mapping/Montreal Neurological Institute space. We then computed the voxel-wise probability distribution map of the SCI using the activation likelihood estimation approach. RESULTS: SCI were more commonly found in the cortex. In supratentorial regions, SCI selectively involved cortical border zone between anterior, middle, and posterior cerebral arteries; in infratentorial regions, distal territory of posteroinferior cerebellar artery. Possible explanations include selective embolization, linked to the vascular anatomy of pial arteries supplying those territories, reduced clearance of emboli in a relatively hypoperfused zone, or a combination of both. This particular distribution of lesions has been reported in both animal models and in patients with microemboli of different sources. CONCLUSIONS: A selective vulnerability of cortical border zone to microemboli occurring during atrial fibrillation transcatheter ablation was observed. We hypothesize that such selectivity may apply to microemboli of different sources.
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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 | 0.000 |
| 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) | 0.002 | 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".