Factors Predicting Hemorrhagic Complications after Multimodal Reperfusion Therapy for Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: We sought to find predictors for hemorrhagic complications in patients with acute ischemic stroke treated with multimodal endovascular therapy. MATERIALS AND METHODS: We retrospectively reviewed patients with acute ischemic stroke treated with multimodal endovascular therapy from May 1999 to March 2006. We reviewed clinical and angiographic data, admission CT Alberta Stroke Programme Early CT Score (ASPECTS), and the therapeutic endovascular interventions used. Posttreatment CT scans were reviewed for the presence of a parenchymal hematoma or hemorrhagic infarction based on defined criteria. Predictors for these types of hemorrhages were determined by logistic regression analysis. RESULTS: We identified 185 patients with a mean age of 65+/-13 years and mean National Institutes of Health Stroke Scale score of 17+/-4. Sixty-nine patients (37%) developed postprocedural hemorrhages: 24 (13%) parenchymal hematomas and 45 (24%) hemorrhagic infarctions. Patients with tandem occlusions (odds ratio [OR] 4.6 [1.4-6.5], P<.016), hyperglycemia (OR 2.8 [1.1-7.7], P<.043), or treated concomitantly with intravenous (IV) tissue plasminogen activator (tPA) and intra-arterial (IA) urokinase (OR 5.1 [1.1-25.0], P<.041) were at a significant risk for a parenchymal hematoma. Hemorrhagic infarction occurred significantly more in patients presenting with an ASPECTS<or=7 (OR 1.9 [1.3-2.7], P<.01). CONCLUSIONS: Hemorrhagic infarctions are related to the extent of infarct based on presentation CT, whereas parenchymal hematomas are associated with the presence of tandem occlusions, hyperglycemia, and treatment with both IV tPA and IA urokinase in patients with acute stroke treated with multimodal endovascular therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".