Abstract P364: Use of the Electronic Alberta Stroke Program Early CT Score Software to Guide Treatment of Patients With Acute Ischemic Stroke
Bibliographic record
Abstract
Introduction: Rapid recognition of large-vessel middle cerebral artery (lvMCA) stroke in patients with acute stroke symptoms is critical to guide thrombectomy and hemicraniectomy decisions. The Electronic Alberta Stroke Program Early CT Score (e-ASPECTS; Brainomix, LLC) is an automated, artificial intelligence software which quantifies acute ischemic volume (AIV) on CT head scans in the MCA territory. In this study, we investigate if e-ASPECTS-derived AIV could help guide treatment and predict outcomes for patients transferred from community hospitals. Hypothesis: E-ASPECTS can help identify patients that may benefit from thrombectomy or hemicraniectomy. Methods: We performed a retrospective chart review on patients age 18-90 transferred to our comprehensive stroke center (CSC) between 2013-2017. Non-contrast CT head scans performed at community hospitals prior to transfer were processed by e-ASPECTS to calculate AIV. Logistic regressions were used to test the relationship between AIV and eventual treatment (thrombectomy, hemicraniectomy). Results: 228 patient CT scans were analyzed by e-ASPECTS. In all transferred patients, higher AIV predicted patients with later confirmed lvMCA strokes (defined as an ICA or M1 occlusion; OR 1.03, CI 1.02-1.05, P<0.001). Higher AIV also trended toward thrombectomy but was not statistically significant (P=0.15). In the subgroup analysis of patients later confirmed to have lvMCA strokes, lower AIV was predictive for thrombectomy (OR 0.95, CI 0.92-0.97, P<0.001). Additionally, higher AIV predicted outcomes of malignant cerebral edema (MCE; OR 1.03, CI 1.02-1.05, P<0.001) and hemicraniectomy (OR 1.04, CI 1.00-1.07, P=0.03). Conclusions: Our study suggests that e-ASPECTS may be useful in identifying patients who would, or would not, benefit from transfer to a CSC from hospitals without thrombectomy or hemicraniectomy resources. Patients with stroke mimics or lvMCA strokes with large penumbras have lower AIVs, while patients with higher AIVs are at risk for MCE and may benefit from hemicraniectomy.
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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 | 0.002 | 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".