E-051 Regional differences in infarction among stroke patients with low aspects: a retrospective cohort study
Bibliographic record
Abstract
Introduction/Purpose The Alberta Stroke Program Early CT Score (ASPECTS) has been widely used to identify patients eligible for mechanical thrombectomy (MT) in ischemic stroke due to large vessel occlusion. Recent randomized controlled trials showed that patients with low baseline ASPECTS can still achieve good long-term functional following MT compared to conservative management. In this study, we investigate which brain regions are more affected in stroke patients with low ASPECTS (2-5). Materials and Methods This retrospective cohort study utilized data from our stroke database at the Medical University of South Carolina (MUSC) from 2013 to 2023. Patients with low ASPECTS (2 - 5) who underwent MT of the internal carotid artery (ICA) or middle cerebral artery (M1) occlusion were included in the study. We assessed the status of individual ASPECTS regions. We categorized the ASPECTS regions into three groups based on the least to the most affected regions. Group 1 included M6, caudate, M4, and M3. Group 2 included m5, m1, and internal capsule. Group 3 represented the most affected regions, including insular ribbon. M2, and lentiform. Results 42 patients were included. Median age of 67.78 years and a median admission NIHSS score of 19.50. The analysis revealed that the M6 and Caudate regions were the least affected areas (27.66% and 40.43%, respectively), whereas the M2 and insular ribbon regions were the most affected areas (78.72% and 76.6%, respectively). Comparing the three categories showed that group 1 was significantly less affected compared with the other two groups (p-value = 0.04). Regression analysis indicated that caudate infarction was significantly associated with intracranial hemorrhage (OR 8.30; 95% CI 2.06 - 43.6; p-value 0.005). Conclusion Brain regions are differentially affected in patients with low ASPECTS, but whether this variability contributes to long-term outcomes and should guide treatment decisions requires further investigation with a larger sample size. Disclosures M. Mahdi Sowlat: None. S. Samir Elawady: None. E. Bass: None. S. Lin: None. A. M Spiotta: None. S. Al Kasab: None.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".