Prognostic importance of CT ASPECTS and CT perfusion measures of infarction in anterior emergent large vessel occlusions
Bibliographic record
Abstract
Background Age, neurologic deficits, core volume (CV), and clinical core or radiographic mismatch are considered in selection for endovascular therapy (ET) in anterior circulation emergent large vessel occlusion (aELVO). Semiquantitative CV estimation by Alberta Stroke Programme Early CT Score (CT ASPECTS) and quantitative CV estimation by CT perfusion (CTP) are both used in selection paradigms. Objective To compare the prognostic value of CTP CV with CT ASPECTS in aELVO. Methods Patients in an institutional endovascular registry who had aELVO, pre-ET National Institutes of Health Stroke Scale (NIHSS) score, non-contrast CT head and CTP imaging, and prospectively collected 3-month modified Rankin Scale (mRS) score were included. Age- and NIHSS-adjusted models, including either CT ASPECTS or CTP volumes (relative cerebral blood flow <30% of normal tissue, total hypoperfusion, and radiographic mismatch), were compared using receiver operator characteristic analyses. Results We included 508 patients with aELVO (60.8% M1 middle cerebral artery, 34% internal carotid artery, mean age 64.1±15.3 years, median baseline NIHSS score 16 (12–20), median baseline CT ASPECTS 8 (7–9), mean CV 16.7±24.8 mL). Age, pre-ET NIHSS, CT ASPECTS, CV, hypoperfusion, and perfusion imaging mismatch volumes were predictors of good outcome (mRS score 0–2). There were no differences in prognostic accuracies between reference (age, baseline NIHSS, CT ASPECTS; area under the curve (AUC)=0.76) and additional models incorporating combinations of age, NIHSS, and CTP metrics including CV, total hypoperfusion or mismatch volume (AUCs 0.72–0.75). Predicted outcomes from CT ASPECTS or CTP CV-based models had excellent agreement (R 2 =0.84, p<0.001). Conclusions Incorporating CTP measures of core or penumbral volume, instead of CT ASPECTS, did not improve prognostication of 3-month outcomes, suggesting prognostic equivalence of CT ASPECTS and CTP CV.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".