{"id":"W4415407782","doi":"10.2196/80351","title":"Rapid Liver Fibrosis Evaluation Using the UNet-ResNet50-32 × 4d Model in Magnetic Resonance Elastography: Retrospective Study","year":2025,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Liver Disease Diagnosis and Treatment","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Liver fibrosis; Retrospective cohort study; Magnetic resonance imaging; Workflow; Fibrosis; Segmentation","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.00336946,0.000666587,0.0006541506,0.001558974,0.0002732328,0.0008942931,0.0007994876,0.0007642203,0.001191968],"category_scores_gemma":[0.007205226,0.0003721125,0.0007809775,0.0006081568,0.0004946702,0.000680449,0.0007059257,0.0005985182,0.0006625222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006852738,"about_ca_system_score_gemma":0.0006428423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005421788,"about_ca_topic_score_gemma":0.006936661,"domain_scores_codex":[0.9990972,0.0003279977,0.00009914865,0.0002325978,0.000182818,0.00006033534],"domain_scores_gemma":[0.9973816,0.0009607209,0.0003066133,0.0005883061,0.0006348712,0.0001278024],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004184015,0.0008336651,0.6415714,0.0008046504,0.001050599,0.00409242,0.0006459453,0.1082799,0.01407849,0.001629098,0.009477207,0.2133526],"study_design_scores_gemma":[0.0001951198,0.002505231,0.2143276,0.0004009704,0.000774746,0.007815875,0.0007529145,0.743853,0.01517276,0.001938884,0.01207327,0.0001895792],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9628994,0.001565994,0.02985099,0.0001957264,0.00006717537,0.0002089977,0.003315261,0.0004505415,0.001445897],"genre_scores_gemma":[0.9790647,0.000437608,0.01407402,0.00007611391,0.00003204917,0.0001097745,0.005752896,0.00008042129,0.0003722979],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005421788,"threshold_uncertainty_score":0.01781964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02810295389429822,"score_gpt":0.3316564061483089,"score_spread":0.3035534522540106,"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."}}