Abstract WP50: Computed Tomography Angiography is Better at Predicting Lesion Core Volume in Acute Ischemic Stroke Patients with Proximal Occlusion
Bibliographic record
Abstract
Introduction: The Alberta Stroke Program Early CT Score (ASPECTS) is a validated tool measuring early ischemic changes on noncontrast CT (NCCT). We hypothesized an ASPECTS using CT angiography-source images (CTA-SI) would be superior to NCCT for predicting lesion core in patients with acute ischemic stroke (AIS). Methods: We included AIS patients from 2010-2014 with M1 middle cerebral artery (MCA) occlusion, NCCT, CTA-SI, and CT perfusion (CTP). Two raters through consensus assigned an ASPECTS to both NCCT and CTA-SI. CTP lesion core was independently determined with the Olea Sphere, OsiriX, and Siemens syngo.via software. MRI lesion core (diffusion weighted imaging (DWI) MRI lesion within 3 days of stroke onset) was measured with the Olea Sphere software. Statistical comparisons between continuous and ordinal variables were performed with Spearman’s rank correlation coefficient and between continuous variable with linear regression. Results: We included 61 patients in the final analysis, of which 24 also had MRI. The mean±SD age was 61±18 years and 61% were male. Mean NIH Stroke Scale at admission was 14.1±8.0 and median (IQR) follow-up modified Rankin Scale was 3 (1,6). The CTA-SI ASPECTS had superior correlation with lesion volume on all 3 software platforms compared to the NCCT ASPECTS; CTA-SI ASPECTS showed a significant correlation with MRI lesion core volume while NCCT ASPECTS did not (see Table 1). Discussion: CTA-SI likely shows regions with less cerebral blood flow that might not yet be hypodense on NCCT. Our results suggest that in patients with AIS and proximal MCA occlusion CTA-SI ASPECTS better predicts lesion core volume, itself an independent predictor of clinical outcome, defined on both CTP and MRI DWI compared with NCCT ASPECTS.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".