Correlation of Alberta Stroke Program Early Computed Tomography Score With Computed Tomography Perfusion Core in Large Vessel Occlusion in Delayed Time Windows
Bibliographic record
Abstract
Background and Purpose: The Alberta Stroke Program Early Computed Tomography (CT) Score (ASPECTS) and CT perfusion (CTP) are commonly used to predict the ischemic core in acute ischemic strokes. CT angiography source images (CTA-SI) can also provide additional information to identify the extent of ischemia. Our objective was to investigate the correlation of noncontrast CT (NCCT) ASPECTS and CTA-SI ASPECTS with CTP core volumes. Methods: We utilized a single institutional, retrospective registry of consecutive patients with acute ischemic stroke with large vessel occlusion between May 2016 and May 2018. We graded ASPECTS both on baseline NCCT and CTA-SI and measured CTP core using automated RAPID software (cerebral blood flow <30%). We used Spearman’s correlation coefficients to evaluate the correlation between continuous variables. Results: A total of 52 patients fit the inclusion criteria of large vessel occlusion in 6 to 24 hours and baseline imaging work up of NCCT, CTA, and CTP. The median age was 63 (interquartile range=53.5–75) and 38.46% were female. The median NCCT ASPECTS was 7 (interquartile range=6–9), CTA-SI ASPECTS was 5 (interquartile range=4–7), and CTP core was 14.5 mL (interquartile range=0–46 mL). There was a moderate correlation between NCCT ASPECTS and CTP core (r s =−0.55, P <0.0001) and between CTA-SI ASPECTS and CTP core (r s =−0.50, P =0.0002). The optimal NCCT ASPECTS cutoff score to detect CTP core ≤70 mL was ≥6 (sensitivity, 0.84; specificity, 0.57; positive predictive value, 0.93; negative predictive value, 0.36) and the optimal CTA-SI ASPECTS was ≥5 (sensitivity, 0.76; specificity, 0.71; positive predictive value, 0.94; negative predictive value, 0.31). Conclusions: There was a moderate correlation between NCCT and CTA-SI ASPECTS in predicting CTP defined ischemic core in delayed time windows. Further studies are needed to determine if NCCT and CTA imaging could be used for image-based patient selection when CTP imaging is not available.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".