Determinants of Leptomeningeal Collateral Status Variability in Ischemic Stroke Patients
Bibliographic record
Abstract
ABSTRACT: Background: Collateral status is an indicator of a favorable outcome in stroke. Leptomeningeal collaterals provide alternative routes for brain perfusion following an arterial occlusion or flow-limiting stenosis. Using a large cohort of ischemic stroke patients, we examined the relative contribution of various demographic, laboratory, and clinical variables in explaining variability in collateral status. Methods: Patients with acute ischemic stroke in the anterior circulation were enrolled in a multi-center hospital-based observational study. Intracranial occlusions and collateral status were identified and graded using multiphase computed tomography angiography. Based on the percentage of affected territory filled by collateral supply, collaterals were graded as either poor (0–49%), good (50–99%), or optimal (100%). Between-group differences in demographic, laboratory, and clinical factors were explored using ordinal regression models. Further, we explored the contribution of measured variables in explaining variance in collateral status. Results: 386 patients with collateral status classified as poor (n= 64), good (n= 125), and optimal (n= 197) were included. Median time from symptom onset to CT was 120 (IQR: 78–246) minutes. In final multivariable model, male sex (OR 1.9, 95% CIs [1.2, 2.9],p= 0.005) and leukocytosis (OR 1.1, 95% CIs [1.1, 1.2],p= 0.001) were associated with poor collaterals. Measured variables only explained 44.8–53.0% of the observed between-patient variance in collaterals. Conclusion: Male sex and leukocytosis are associated with poorer collaterals. Nearly half of the variance in collateral flow remains unexplained and could be in part due to genetic differences.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".