{"id":"W2953640564","doi":"10.1002/jmri.26850","title":"Rapid quantitative susceptibility mapping of intracerebral hemorrhage","year":2019,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Intracerebral and Subarachnoid Hemorrhage Research","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Quantitative susceptibility mapping; Artifact (error); Intraclass correlation; Intracerebral hemorrhage; Medicine; Nuclear medicine; Susceptibility weighted imaging; Correlation; Magnetic resonance imaging; Radiology; Artificial intelligence; Mathematics; Surgery; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001382903,0.0002792189,0.0001615675,0.00124029,0.0000885988,0.0002580647,0.0001993868,0.0001314851,0.001107335],"category_scores_gemma":[0.004055139,0.0001058602,0.0001249873,0.0003022227,0.0002646089,0.0002486204,0.0002450228,0.0001334965,0.0001468988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001820423,"about_ca_system_score_gemma":0.0001745791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000350287,"about_ca_topic_score_gemma":0.0003269126,"domain_scores_codex":[0.99963,0.0001756102,0.00003197243,0.00004671518,0.00009038226,0.00002544513],"domain_scores_gemma":[0.9983931,0.0006133468,0.000493241,0.0001288517,0.0002990057,0.00007243617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003815332,0.0002072114,0.4726381,0.000940026,0.0003182028,0.002255311,0.0008395328,0.004191324,0.3364404,0.0006842103,0.0009283437,0.176742],"study_design_scores_gemma":[0.0001166781,0.002360256,0.9065556,0.00008098382,0.0001309556,0.01371819,0.0002599646,0.008800516,0.06478482,0.001220196,0.001914813,0.00005702152],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9852412,0.0008321034,0.0128919,0.00002865758,0.00000544348,0.00006150247,0.0001424667,0.00006887911,0.0007278377],"genre_scores_gemma":[0.9932491,0.0001904396,0.006245132,0.000007609691,0.0000097299,0.00003181197,0.0001249871,0.000009558163,0.0001316597],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001382903,"threshold_uncertainty_score":0.00731355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01981168106083734,"score_gpt":0.2861666676474285,"score_spread":0.2663549865865912,"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."}}