A Critical Review of Alberta Stroke Program Early CT Score for Evaluation of Acute Stroke Imaging
Bibliographic record
Abstract
Assessment of ischemic stroke lesions on computed tomography (CT) or MRI using the Alberta Stroke Program Early CT Score (ASPECTS) is widely used to guide acute stroke treatment. We aimed to review the current evidence on ASPECTS. Originally, the score was developed for standardized lesion assessment on non-contrast CT (NCCT). Early studies described ASPECTS as a predictor of functional outcome and symptomatic intracranial hemorrhage after iv-thrombolysis with a threshold of ≤7 suggested to identify patients at high risk. Following studies rather pointed toward a linear relationship between ASPECTS and functional outcome. ASPECTS has also been applied to assess perfusion CT and diffusion-weighted MRI (DWI). Cerebral blood volume ASPECTS proved to be the best predictor of outcome, outperforming NCCT-ASPECTS in some studies. For DWI-ASPECTS varying thresholds to identify patients at risk for poor outcome were reported. ASPECTS has been used for patient selection in three of the five groundbreaking trials proving efficacy of mechanical thrombectomy published in 2015. ASPECTS values predict functional outcome after thrombectomy. Moreover, treatment effect of thrombectomy appears to depend on ASPECTS values being smaller or not present in low ASPECTS, while patients with ASPECTS 5-10 do clearly benefit from mechanical thrombectomy. However, as patients with low ASPECTS values were excluded from recent trials data on this subgroup is limited. There are several limitations to ASPECTS addressed in a growing number of studies. The score is limited to the anterior circulation, the template is unequally weighed and correlation with lesion volume depends on lesion location. Overall ASPECTS is a useful and easily applicable tool for assessment of prognosis in acute stroke treatment and to help guide acute treatment decisions regardless whether MRI or CT is used. Patients with low ASPECTS values are unlikely to achieve good outcome. However, methodological constraints of ASPECTS have to be considered, and based on present data, a clear cutoff value to define "low ASPECTS values" cannot be given.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".