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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".