Abstract P538: A Detailed Analysis of Intracranial Hemorrhage After Endovascular Treatment in Acute Ischemic Stroke Due to Large Vessel Occlusion in the Escape-NA1 Trial
Bibliographic record
Abstract
Purpose: We investigated the prevalence and prognostic impact on outcome of any intracranial hemorrhage, hemorrhage morphology, type and volume in acute ischemic stroke patients undergoing mechanical thrombectomy. Methods: Prevalence of intracranial hemorrhage, hemorrhage type, morphology and volume was determined on 24h follow-up imaging (non contrast head CT or gradient-echo/susceptibility-weighted MRI). Proportions of good outcome (mRS 0-2 at 90 days) were reported for patients with vs. without any intracranial hemorrhage. Multivariable logistic regression with adjustment for key minimization variables and total infarct volume was performed to obtain adjusted effect size estimates for hemorrhage type and volume on good outcome. Results: Hemorrhage on follow up-imaging was seen in 372/1097 (33.9%) patients, among them 126 (33.9%) with hemorrhagic infarction (HI) type 1, 108 (29.0%) with HI-2, 72 /19.4%) with parenchymal hematoma (PH) type 1, 37 (10.0) with PH2, 8 (2.2%) with remote PH and 21 (5.7%) with extra-parenchymal/intraventricular hemorrhage. Good outcomes were less often achieved by patients with hemorrhage on follow-up imaging (164/369 [44.4%] vs. 500/720 [69.4%]). Any type of intracranial hemorrhage was strongly associated with decreased chances of good outcome ( adj OR 0.62 [CI 95 0.44 - 0.87]). The effect of hemorrhage was driven by both PH hemorrhage sub-type [PH-1 ( adj OR 0.39 [CI 95 0.21 - 0.72]), PH-2 ( adj OR 0.15 [CI 95 0.05 - 0.50])] and extra-parenchymal/intraventricular hemorrhage ( adj OR 0.60 (0.20-1.78) Petechial hemorrhages (HI-1 and HI-2) were not associated with poorer outcomes. Hemorrhage volume ( adj OR 0.97 [CI 95 0.05 - 0.99] per ml increase) was significantly associated with decreased chances of good outcome. Conclusion: Presence of any hemorrhage on follow-up imaging was seen in one third of patients and strongly associated with decreased chances of good outcome.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".