Large ischemic core defined by visually assessed ASPECTS predicts functional outcomes comparably accurate to automated CT perfusion in the 6–24 h window
Bibliographic record
Abstract
Introduction Automated CT perfusion (aCTP) is commonly used to select patients with anterior circulation large vessel occlusion (aLVO) for endovascular treatment (EVT). The equivalence of visually assessed Non-contrast CT Alberta Stroke Program Early CT Scores (ASPECTS) and aCTP based selection in predicting favorable functional outcomes remains uncertain. Patients and methods Retrospective multicenter study of adult aLVO patients from the Swiss Stroke Registry (2014–2021) treated with EVT or best medical treatment 6–24 h after stroke onset. We assessed ASPECTS on non-contrast CT visually and ischemic core volumes on aCTP, defining ASPECTS 0–5 and aCTP CBF < 30% volumes ⩾50 mL as large ischemic cores. We used logistic regression to explore the association between CT modalities and favorable functional outcomes (modified Rankin Scale [mRS] score shift toward lower categories) at 3 months. Receiver operating characteristic (ROC) curve analysis compared the predictive accuracy of visually assessed ASPECTS and aCTP ischemic core for favorable outcomes (mRS 0–2) at 3 months. Results Of 210 patients, 11.4% had ASPECTS 0–5, and 12.9% aCTP core volumes ⩾50 mL. Within the same model, ASPECTS but not aCTP core volumes were associated with favorable outcomes (ASPECTS: acOR 1.85, 95%CI 1.27–2.70, p = 0.001). The ROC curve analyses showed comparable diagnostic accuracy in predicting favorable functional outcomes (mRS 0–2) at 3 months (ROC areas: ASPECTS 0.80 [95%CI 0.74–0.86] vs aCTP core 0.79 [95%CI 0.72–0.85]). Discussion and conclusion In patients with aLVO, visually assessed ASPECTS showed at least comparable accuracy to automatically generated CTP core volumes in predicting functional outcomes at 3 months.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".