Abstract Number: LBA14 Incidence of Intracranial Hemorrhage & Clinical Outcomes in Fast Versus Slow Progressors as per ASPECTS
Bibliographic record
Abstract
Introduction Heterogeneity of collateralization in patients with acute ischemic stroke (AIS) is a marker of fast versus slow progression of penumbral consumption and infarct expansion. Our aim was to evaluate the relationship between time‐dependent stroke progression and incidence of intracranial hemorrhage (ICH) and acute neurological deficits. Methods Retrospective chart review of patients presenting with anterior circulation large vessel occlusion (LVO)‐associated AIS at our comprehensive stroke center with 24 hours last known normal (LKN) who underwent endovascular thrombectomy (EVT) without intra‐arterial thrombolytics or non‐thrombolytics were included. We used Alberta Stroke Program Early CT Score (ASPECTS) on initial non‐contrast CT to identify slow versus fast progressors. ASPECTS < = 7 was defined as fast progressor. Subgroup analysis was performed based on LKN < 3 hours, 3–9 hours, and 9–24 hours. We evaluated rate of hemorrhagic transformation using ECASS‐3 criteria and determined change in NIHSS from baseline to discharge. Mann‐Whitney U test and Fisher exact test statistic with Social science statistics software used for data analysis. Results From September 2019 to December 2021, out of 268 subjects who underwent EVT, 48 met inclusion criteria. Mean age was 65.29 (95% CI 61.32, 69.27), and median presenting NIHSS was 16 (95% CI 14.39, 18.40). Mean ASPECTS was 7.71 (95% CI 7.21, 8.20). There was significant difference is hemorrhagic transformation rate between ASPECTS >7 and < = 7 (Fisher value = 0.018). Sample size was not large enough to perform subgroup analysis based on last known normal. However, there was a trend towards increase in hemorrhagic transformation rate with greater time from last known well at same ASPECTS score. Slow progressors also had a significant improvement in presenting and discharge NIHSS as compared to fast progressors (z‐score is ‐3.10, p‐value is 0.002). Conclusions Our study suggests that ASPECTS score as assessed in different time windows to differentiate fast versus slow progressors is not only a predictor of clinical outcome, but also independently associated with risk of hemorrhagic transformation. Larger, prospective studies are needed to validate our results.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.015 | 0.002 |
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".