{"id":"W4402139978","doi":"10.1002/alz.14207","title":"Utility of cerebrovascular imaging biomarkers to detect cerebral amyloidosis","year":2024,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Intracerebral and Subarachnoid Hemorrhage Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; National Institutes of Health; Servier; GE Healthcare; BioClinica; Fujirebio US; National Institute of Mental Health; Novartis Pharmaceuticals Corporation; Biogen; Takeda Pharmaceutical Company; Eli Lilly and Company; Bristol-Myers Squibb; Roche; Alzheimer's Drug Discovery Foundation; National Institute on Aging; Alzheimer's Association; U.S. Department of Defense","keywords":"Hyperintensity; Positron emission tomography; Neuroimaging; Medicine; Magnetic resonance imaging; White matter; Alzheimer's Disease Neuroimaging Initiative; Cognitive decline; Internal medicine; Logistic regression; Disease; Alzheimer's disease; Cardiology; Pathology; Dementia; Radiology; Psychiatry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003144516,0.0008642161,0.0006275139,0.002053577,0.0002276253,0.001803438,0.0005691045,0.000614481,0.001737794],"category_scores_gemma":[0.00853994,0.0002866627,0.0005243774,0.0009297259,0.0003384356,0.0007138056,0.0003931217,0.0007274484,0.0005526488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004239454,"about_ca_system_score_gemma":0.0006491223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002203722,"about_ca_topic_score_gemma":0.003316561,"domain_scores_codex":[0.999059,0.000416711,0.0001033249,0.0001981531,0.0001641724,0.00005859591],"domain_scores_gemma":[0.9940639,0.002111023,0.001802194,0.0002752961,0.001354335,0.0003932584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005321919,0.00009288042,0.9690347,0.0001175942,0.0004109751,0.0001440176,0.00002963658,0.0004698781,0.0008946593,0.0001162512,0.001314293,0.02684293],"study_design_scores_gemma":[0.00006807489,0.0005328209,0.9835035,0.0002483573,0.0005741653,0.001876043,0.0001314146,0.007304403,0.0021111,0.00124395,0.002370215,0.00003598348],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9582008,0.02664284,0.003480598,0.001140449,0.000191394,0.000118146,0.002495535,0.0001542206,0.007576124],"genre_scores_gemma":[0.9932742,0.002615005,0.002468388,0.0002091974,0.0001315073,0.00004585659,0.0007803316,0.0000104494,0.0004649798],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003144516,"threshold_uncertainty_score":0.01662999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0229256702449152,"score_gpt":0.2957151847436756,"score_spread":0.2727895144987604,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}