ASPECTS (Alberta Stroke Program Early CT Score) Measurement Using Hounsfield Unit Values When Selecting Patients for Stroke Thrombectomy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The ASPECTS (Alberta Stroke Program Early CT Score) is a quantitate score that measures the extent of early ischemic changes. Our aim was to investigate how measurement of ASPECTS using Hounsfield unit (HU) values on initial noncontrast head computerized tomography (CT) correlates with the extent of final infarct on follow-up imaging. METHODS: Cases of acute stroke from the middle cerebral artery M1 occlusion in which complete recanalization (TICI [Thrombolysis in Cerebral Infarction] 3) was achieved were included for analysis. Using HU ratio (HU affected/HU control hemisphere) and HU difference (HU control-HU affected hemisphere) values, ASPECTS was measured on initial CT imaging and correlated with final ASPECTS at 24 hours. The study cohort consisted of 41 patients with acute stroke from the M1 occlusion. The mean time from stroke symptoms onset to baseline head CT imaging was 264 minutes and from CT to TICI 3 recanalization was 142 minutes. RESULTS: <0.0001) and the lowest mean and median absolute errors (1.4 and 1, respectively). CONCLUSIONS: We established a simple algorithm for rapid and accurate assessment of ASPECTS on baseline CT imaging to predict the extent of final stroke in patients with emergent large vessel occlusion who undergo endovascular revascularization.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".