{"id":"W2566230053","doi":"10.1002/jmri.25550","title":"Liver fibrosis: Review of current imaging and MRI quantification techniques","year":2016,"lang":"en","type":"review","venue":"Journal of Magnetic Resonance Imaging","topic":"Liver Disease Diagnosis and Treatment","field":"Medicine","cited_by":198,"is_retracted":false,"has_abstract":true,"ca_institutions":"Philips (Canada); McGill University Health Centre; Université de Montréal; CARE Canada; Centre Hospitalier de l’Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Medicine; Elastography; Magnetic resonance imaging; Cirrhosis; Chronic liver disease; Liver biopsy; Fibrosis; Radiology; Liver disease; Hepatic fibrosis; Magnetic resonance elastography; Liver fibrosis; Pathology; Ultrasound; Biopsy; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000610344,0.0003540879,0.001829584,0.0002982492,0.00003814207,0.00003146442,0.0001830222,0.00004364232,0.0001241739],"category_scores_gemma":[0.0001759512,0.0002160348,0.0006422144,0.0001866776,0.000140722,0.0001858131,0.00007140642,0.000278321,0.0000097949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000151328,"about_ca_system_score_gemma":0.0003867281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003905006,"about_ca_topic_score_gemma":7.401459e-8,"domain_scores_codex":[0.9974006,0.0001648202,0.001414731,0.0003055699,0.0004937707,0.0002204961],"domain_scores_gemma":[0.9971285,0.0002065435,0.001598405,0.0003962373,0.0004965938,0.0001736881],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001223149,0.0001508407,0.0006625123,0.08396526,0.00003196103,0.00009379235,0.00001222423,7.096649e-10,0.000003154149,0.00002576115,0.006799157,0.9082431],"study_design_scores_gemma":[0.0002267056,0.00007249541,0.0009015232,0.4199381,0.001817892,0.0004498945,0.000003283857,0.000001676324,0.00001364403,0.00001318372,0.5764508,0.0001108208],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000002704455,0.9980778,0.00007998341,0.0006111415,0.0001639228,0.0008466986,0.00004425439,0.00001327667,0.0001602952],"genre_scores_gemma":[0.00001818184,0.9980077,0.001448118,0.0001095531,0.0002649005,0.00005649367,0.00001065379,0.00004140428,0.00004299526],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9081323,"threshold_uncertainty_score":0.8809645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03227932396786264,"score_gpt":0.354874708177212,"score_spread":0.3225953842093493,"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."}}