Fulltext embargoed for: 12 months post date of publicationBACKGROUND AND PURPOSE: Several prognostic scores have been developed to predict the risk of symptomatic intracranial hemorrhage (sICH) after ischemic stroke thrombolysis. We compared the performance of these scores in a multicenter cohort. METHODS: We merged prospectively collected data of patients with consecutive ischemic stroke who received intravenous thrombolysis in 7 stroke centers. We identified and evaluated 6 scores that can provide an estimate of the risk of sICH in hyperacute settings: MSS (Multicenter Stroke Survey); HAT (Hemorrhage After Thrombolysis); SEDAN (blood sugar, early infarct signs, [hyper]dense cerebral artery sign, age, NIH Stroke Scale); GRASPS (glucose at ...
BackgroundThis study aimed to compare the performance of different machine learning models in predic...
<p><b>Background:</b> Two clinical risk scores, the Haemorrhage After Thrombolysis and Multicentre S...
BackgroundThis study aimed to compare the performance of different machine learning models in predic...
BACKGROUND AND PURPOSE: Several prognostic scores have been developed to predict the risk of symptom...
OBJECTIVE: A study was undertaken to develop a score for assessing risk for symptomatic intracranial...
The SEDAN score is a prediction rule for assessment of the risk of symptomatic intracerebral hemorrh...
Background and Purpose-Symptomatic intracerebral hemorrhage (SICH) is a serious complication in pati...
Item does not contain fulltextBACKGROUND: Balancing the risks of recurrent ischaemic stroke and intr...
Background and Purpose: There is limited information on symptomatic intracranial hemorrhage (sICH) i...
Background: To assess the utility of previously developed scoring systems, we compared SEDAN, named ...
Background and Purpose—Symptomatic intracerebral hemorrhage (SICH) is a serious complication in pati...
Background<p>Symptomatic intracranial hemorrhage (sICH) after intravenous thrombolysis with recombin...
Objective: We evaluated the reliability of eight clinical prediction models for symptomatic intracer...
BackgroundSymptomatic intracranial hemorrhage (sICH) after intravenous thrombolysis with recombinant...
BackgroundThis study aimed to compare the performance of different machine learning models in predic...
BackgroundThis study aimed to compare the performance of different machine learning models in predic...
<p><b>Background:</b> Two clinical risk scores, the Haemorrhage After Thrombolysis and Multicentre S...
BackgroundThis study aimed to compare the performance of different machine learning models in predic...
BACKGROUND AND PURPOSE: Several prognostic scores have been developed to predict the risk of symptom...
OBJECTIVE: A study was undertaken to develop a score for assessing risk for symptomatic intracranial...
The SEDAN score is a prediction rule for assessment of the risk of symptomatic intracerebral hemorrh...
Background and Purpose-Symptomatic intracerebral hemorrhage (SICH) is a serious complication in pati...
Item does not contain fulltextBACKGROUND: Balancing the risks of recurrent ischaemic stroke and intr...
Background and Purpose: There is limited information on symptomatic intracranial hemorrhage (sICH) i...
Background: To assess the utility of previously developed scoring systems, we compared SEDAN, named ...
Background and Purpose—Symptomatic intracerebral hemorrhage (SICH) is a serious complication in pati...
Background<p>Symptomatic intracranial hemorrhage (sICH) after intravenous thrombolysis with recombin...
Objective: We evaluated the reliability of eight clinical prediction models for symptomatic intracer...
BackgroundSymptomatic intracranial hemorrhage (sICH) after intravenous thrombolysis with recombinant...
BackgroundThis study aimed to compare the performance of different machine learning models in predic...
BackgroundThis study aimed to compare the performance of different machine learning models in predic...
<p><b>Background:</b> Two clinical risk scores, the Haemorrhage After Thrombolysis and Multicentre S...
BackgroundThis study aimed to compare the performance of different machine learning models in predic...