Alberta Stroke Program Early CT Score Versus Computed Tomographic Perfusion to Predict Functional Outcome After Successful Reperfusion in Acute Ischemic Stroke
Bibliographic record
Abstract
Background and Purpose- We aimed to compare the ability of conventional Alberta Stroke Program Early CT Score (ASPECTS), automated ASPECTS, and ischemic core volume on computed tomographic perfusion to predict clinical outcome in ischemic stroke because of large vessel occlusion ≤18 hours after symptom onset. Methods- We selected patients with acute ischemic stroke from the CRISP study (Computed Tomographic Perfusion to Predict Response to Recanalization in Ischemic Stroke Project) with successful reperfusion (modified treatment in cerebral ischemia score 2b or 3). We used e-ASPECTS software to calculate automated ASPECTS and RAPID software to estimate ischemic core volumes. We studied associations between these imaging characteristics and good outcome (modified Rankin Scale score, 0-2) or poor outcome (modified Rankin Scale score, 4-6) in univariable and multivariable analysis, after adjustment for relevant clinical confounders. Results- We included 156 patients. Conventional and automated ASPECTS was not associated with good or poor outcome in univariable analysis ( P=nonsignificant for all). Automated ASPECTS was associated with good outcome in multivariable analysis ( P=0.02) but not with poor outcome. Ischemic core volume was associated with good ( P<0.01) and poor outcome ( P=0.04) in univariable and multivariable analysis ( P=0.03 and P=0.02, respectively). Computed tomographic perfusion predicted good outcome with an area under the curve of 0.62 (95% CI, 0.53-0.71) and optimal cutoff core volume of 15 mL. Conclusions- Ischemic core volume assessed on computed tomographic perfusion is a predictor of clinical outcome among patients in whom endovascular reperfusion is achieved ≤18 hours after symptom onset. In this population, conventional or automated ASPECTS did not predict outcome.
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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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".