Prognosis and risk factors associated with asymptomatic intracranial hemorrhage after endovascular treatment of large vessel occlusion stroke: a prospective multicenter cohort study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Asymptomatic intracranial hemorrhage (aICH) is a common occurrence after endovascular treatment (EVT) for acute ischemic stroke (AIS). The aims of this study were to address its impact on 3-month functional outcome and to identify risk factors for aICH after EVT. METHODS: Patients with AIS attributable to anterior circulation large vessel occlusion who underwent EVT were enrolled in a multicenter prospective registry. Based on imaging performed 22-36 h post-EVT, we included patients with no intracranial hemorrhage (ICH) or aICH. Poor outcome defined as a 3-month modified Rankin Scale (mRS) score 4-6 and overall 3-month mRS score distribution were compared according to presence/absence of aICH, and aICH subtype using logistic regression. We assessed the risk factors of aICH using a multivariate logistic regression model. RESULTS: Of the 1526 patients included in the study, 653 (42.7%) had aICH. Patients with aICH had a higher rate of poor outcome: odds ratio (OR) 1.88 (95% confidence interval [CI] 1.44-2.44). Shift analysis of mRS score found a fully adjusted OR of 1.79 (95% CI 1.47-2.18). Hemorrhagic infarction (OR 1.63 [95% CI 1.22-2.18]) and parenchymal hematoma (OR 2.99 [95% CI 1.77-5.02]) were associated with higher risk of poor outcome. Male sex, diabetes, coronary artery disease, baseline National Institutes of Health Stroke Scale score and Alberta Stroke Program Early Computed Tomography Score, number of passes and onset to groin puncture time were independently associated with aICH. CONCLUSIONS: Patients with aICH, irrespective of the radiological pattern, have a worse functional outcome at 3 months compared with those without ICH after EVT for AIS. The number of EVT passes and the time from onset to groin puncture are factors that could be modified to reduce deleterious ICH.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".