{"id":"W4210245402","doi":"10.1371/journal.pone.0262291","title":"Quantitative ultrasound, elastography, and machine learning for assessment of steatosis, inflammation, and fibrosis in chronic liver disease","year":2022,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Liver Disease Diagnosis and Treatment","field":"Medicine","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; McGill University Health Centre; Université de Montréal","funders":"Canadian Institutes of Health Research; Fonds de Recherche du Québec - Santé; Fondation de l'Association des radiologistes du Québec; Siemens Healthineers; Institute of Nutrition, Metabolism and Diabetes; McGill University","keywords":"Receiver operating characteristic; Medicine; Steatosis; Elastography; Fibrosis; Fatty liver; Transient elastography; Internal medicine; Gastroenterology; Ultrasound; Radiology; Liver fibrosis; Disease","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01229863,0.001181174,0.0008556458,0.002179725,0.0002509228,0.0009064759,0.0004870644,0.00073006,0.0006202482],"category_scores_gemma":[0.02435813,0.0002029854,0.0006882731,0.0006915352,0.0006661024,0.0008020198,0.0006316081,0.0007787573,0.0002068758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005827381,"about_ca_system_score_gemma":0.0007997494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001857555,"about_ca_topic_score_gemma":0.001830565,"domain_scores_codex":[0.9957407,0.002996613,0.0001973833,0.0003132114,0.0006589548,0.00009322358],"domain_scores_gemma":[0.9907959,0.007001964,0.0007652228,0.0004345436,0.0008337133,0.0001686489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002617675,0.001297407,0.3302129,0.0007043473,0.001570658,0.0001938558,0.0001713904,0.1796432,0.01178772,0.001651317,0.003652283,0.4664972],"study_design_scores_gemma":[0.0001197684,0.001398928,0.07209589,0.0001707087,0.0001990668,0.0003494721,0.00006153753,0.9169382,0.00369455,0.003923485,0.0009971438,0.00005126885],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.559638,0.01131884,0.4217965,0.001768905,0.0002293275,0.0004819678,0.0007249134,0.001254161,0.002787439],"genre_scores_gemma":[0.923046,0.0006872728,0.07488382,0.0002327421,0.0001185657,0.0002395035,0.0003063343,0.00002795499,0.0004577593],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01229863,"threshold_uncertainty_score":0.06504214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03951634381421799,"score_gpt":0.2766731862934642,"score_spread":0.2371568424792462,"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."}}