Stroke etiology and collaterals: atheroembolic strokes have greater collateral recruitment than cardioembolic strokes
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Chronic hypoperfusion from athero-stenotic lesions is thought to lead to better collateral recruitment compared to cardioembolic strokes. It was sought to compare collateral flow in stroke patients with atrial fibrillation (AF) versus stroke patients with cervical atherosclerotic steno-occlusive disease (CASOD). METHOD: This was a retrospective review of a prospectively collected endovascular database. Patients with (i) anterior circulation large vessel occlusion stroke, (ii) pre-treatment computed tomography angiography (CTA) and (iii) intracranial embolism from AF or CASOD were included. CTA collateral patterns were evaluated and categorized into two groups: absent/poor collaterals (CTA collateral score 0-1) versus moderate/good collaterals (CTA collateral score 2-4). CT perfusion was also utilized for baseline core volume and evaluation of infarct growth. RESULTS: A total of 122 patients fitted the inclusion criteria, of whom 88 (72%) had AF and 34 (27%) CASOD. Patients with AF were older (P < 0.01) and less often males or smokers (P = 0.04 and P < 0.01 respectively). Baseline National Institutes of Health Stroke Scale and Alberta Stroke Program Early CT Score were comparable between groups. Collateral scores were lower in the AF group (P = 0.01) with patients having poor collaterals in 28% of cases versus 9% in the CASOD group (P = 0.03). Mortality rates (20% vs. 0%; P = 0.02) were higher in the AF patients whilst rates of any parenchymal hemorrhage (6% vs. 26%; P < 0.01) were higher in the CASOD group. On multivariable analysis, CASOD was an independent predictor of moderate/good collaterals (odds ratio 4.70; 95% confidence interval 1.17-18.79; P = 0.03). CONCLUSIONS: Atheroembolic strokes seem to be associated with better collateral flow compared to cardioembolic strokes. This may in part explain the worse outcomes of AF-related stroke.
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 | 0.002 |
| 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".