Predicting aneurysm rupture probabilities through the application of a computed tomography angiography-derived binary logistic regression model: Clinical article

Journal of Neurosurgery

Volumn 110, Issue 1, 2009, Pages 1-6

  Prestigiacomo, Charles J ^a,b   He, Wenzhuan ^a   Catrambone, Jeffrey ^a   Chung, Stephanie ^a   Kasper, Lydia ^a   Pasupuleti, Latha ^a   Mittal, Neelesh ^a  

^a RUTGERS NEW JERSEY MEDICAL SCHOOL   (United States)

^b NEUROLOGICAL INSTITUTE OF NEW JERSEY   (United States)

Author keywords

Binary logistic regression; Cerebral aneurysm; Subarachnoid hemorrhage

Indexed keywords

ACCURACY; ADULT; AGED; ANEURYSM RUPTURE; ARTICLE; COMPUTED TOMOGRAPHIC ANGIOGRAPHY; CONTROLLED STUDY; FEMALE; HUMAN; LOGISTIC REGRESSION ANALYSIS; MAJOR CLINICAL STUDY; MALE; MATHEMATICAL MODEL; PHYSICAL PHENOMENA; PREDICTION; PRIORITY JOURNAL; PROBABILITY; PROSPECTIVE STUDY; SENSITIVITY ANALYSIS; THREE DIMENSIONAL IMAGING; ALGORITHM; BRAIN ANGIOGRAPHY; CASE REPORT; COMPUTER ASSISTED TOMOGRAPHY; INTRACRANIAL ANEURYSM; MIDDLE AGED; PREDICTION AND FORECASTING; RADIOGRAPHY; STATISTICAL ANALYSIS; STATISTICAL MODEL; SUBARACHNOID HEMORRHAGE;

ADULT; AGED; ALGORITHMS; ANEURYSM, RUPTURED; CEREBRAL ANGIOGRAPHY; DATA INTERPRETATION, STATISTICAL; FEMALE; HUMANS; INTRACRANIAL ANEURYSM; LOGISTIC MODELS; MALE; MIDDLE AGED; MODELS, STATISTICAL; PREDICTIVE VALUE OF TESTS; SUBARACHNOID HEMORRHAGE; TOMOGRAPHY, X-RAY COMPUTED; YOUNG ADULT;

EID: 60749100091     PISSN: 00223085     EISSN: 19330693     Source Type: Journal    
DOI: 10.3171/2008.5.17558     Document Type: Article

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