P-002 Mechanical thrombectomy for patients with stroke presenting with low alberta stroke program early computed tomography score (ASPECTS): early versus late time window outcomes
Bibliographic record
Abstract
Background and Purpose Recent trials showed a clear benefit of mechanical thrombectomy for patients with emergent large vessel occlusion and large core infarction. However, limited number of patients were included in the late window. This study aimed to compare outcomes of low ASPECTS stroke patients who underwent mechanical thrombectomy in the early versus late time window. Methods A retrospective cohort study was conducted using data from the Stroke Thrombectomy and Aneurysm Registry (STAR) from 2013 to 2023. Patients with low ASPECTS (2-5) who underwent mechanical thrombectomy for ICA or M1 stroke were included. Patients were divided into early (within 6 hours) and late (6-24 hours) time window groups. The primary outcome was 90-day favorable outcome (modified Rankin Scale [mRS] score 0-3). Results Among the 10,081 patients who underwent mechanical thrombectomy, 387 met the inclusion criteria. Of those, 219 (56.6%) were treated in the early window. Median (IQR) age was 70.4 (60-78) years, 180 (46.5%) were female, and 221 (68.4%) were White. There was no significant difference in successful recanalization rates (Thrombolysis in Cerebral Infarction [TICI] 2C/3) between the early and late window groups (56.6% versus 52.4%, P=0.478). Additionally, there was no significant difference in favorable outcomes at 90 days between the two groups (25.4% versus 35.9%, P=0.142). In Binary regression analysis, age (OR 0.94; 95% CI 0.92-0.97; P<0.001), admission NIHSS (OR 0.88; 95% CI 0.83-0.94; P<0.001), and successful recanalization (OR 4.01; 95% CI 1.94-8.71; P<0.001) were predictors of a favorable outcome at 90 days. Conclusion Mechanical thrombectomy appears to carry a similar benefit profile among patients treated in the late window as those in the early window. Disclosures S. Samir Elawady: None. M. Mahdi Sowlat: None. I. Maier: None. P. Jabbour: None. J. Kim: None. S. Quintero Wolfe: None. A. Rai: None. R. M Starke: None. M. Psychogios: None. E. Samaniego: None. A. Arthur: None. S. Yoshimura: None. J. A. Grossberg: None. A. Alawieh: None. J. Mascitelli: None. I. Fragata: None. H. Cuellar: None. A. Polifka: None. J. Osbun: None. R. Crosa: None. C. Matouk: None. M. S. Park: None. M. R. Levitt: None. W. Brinjikji: None. T. Dumont: None. R. Williamson Jr: None. P. Navia: 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".