Alberta Stroke Program Early CT Score and collateral status predict target mismatch in large vessel occlusion with delayed time windows
Bibliographic record
Abstract
Background The Alberta Stroke Program Early CT Score (ASPECTS) and collateral score (CS) are two readily available imaging metrics for the evaluation of acute ischemic stroke (AIS) with large vessel occlusion (LVO). Objective To investigate the predictive value of the ASPECTS combined with CS in detecting patients with CT perfusion (CTP) target mismatch in delayed time windows. Methods One hundred and sixty-four patients with LVO-AIS were included. ASPECTS was assessed on non-contrast CT (NCCT). CS was evaluated on single-phase CT angiography (sCTA). Target mismatch was defined as a CTP core volume ≤70 mL, mismatch ratio ≥1.8, and absolute mismatch volume ≥15 mL. Spearman correlation analysis and receiver operating characteristic curve analyses were performed. Results The median NCCT ASPECTS of the 164 patients was 8 (IQR 6–9), median sCTA-CS was 2 (IQR 1–2), and median CTP core volume was 8 mL (IQR 0–29.5). There was a strong correlation between NCCT ASPECTS and CTP core volume (rs=−0.756, p<0.0001) and a moderate correlation between the sCTA-CS and CTP core volume (rs=−0.450, p<0.0001). Integrating NCCT ASPECTS and sCTA-CS provided a higher area under the curve (AUC) for predicting target mismatch (AUC=0.882; sensitivity, 89.1%; specificity, 77.8%; p<0.001). Conclusions NCCT ASPECTS had a strong correlation with CTP core volumes in patients with LVO-AIS in delayed time windows. Combining NCCT ASPECTS with sCTA-CS resulted in a more accurate prediction of target mismatch. If a CTP scan is not available, NCCT ASPECTS combined with sCTA-CS may guide clinicians in making treatment decisions.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".