{"id":"W4391886379","doi":"10.1093/jcag/gwad061.287","title":"A287 LEVERAGING MACHINE LEARNING TO IMPROVE THE DIAGNOSTIC ACCURACY OF ULTRASOUND SCREENING FOR HEPATOCELLULAR CARCINOMA","year":2024,"lang":"en","type":"article","venue":"Journal of the Canadian Association of Gastroenterology","topic":"Liver Disease Diagnosis and Treatment","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Hepatocellular carcinoma; Ultrasound; Computer science; Artificial intelligence; Machine learning; Medical physics; Radiology; Medicine; Cancer research","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":[0.001996417,0.000267479,0.0003714097,0.001893888,0.000148819,0.0006145343,0.0003795508,0.000384622,0.001167049],"category_scores_gemma":[0.01004196,0.0001762455,0.0003228748,0.0006923011,0.0002746667,0.0004628332,0.0003803988,0.0002673574,0.0004171664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003657718,"about_ca_system_score_gemma":0.0003376704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001297429,"about_ca_topic_score_gemma":0.001999399,"domain_scores_codex":[0.9987648,0.0006248425,0.0001570891,0.0001657116,0.0001954712,0.00009215241],"domain_scores_gemma":[0.9943341,0.003402722,0.0009265555,0.0002655216,0.0008260804,0.00024499],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003012838,0.0001346919,0.9299383,0.00009725363,0.00007768213,0.0002234487,0.00004170013,0.002674978,0.003543208,0.00007092254,0.0006909866,0.06220558],"study_design_scores_gemma":[0.00005303389,0.0008031966,0.8356717,0.0001214226,0.0002316915,0.001891035,0.0001881797,0.1478175,0.01027801,0.0007019232,0.002206773,0.00003539229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9877965,0.002623022,0.006707074,0.0005220402,0.00005106282,0.00003999639,0.000312894,0.0001410106,0.001806377],"genre_scores_gemma":[0.9963831,0.0002077724,0.00305559,0.0000390637,0.00003838004,0.000006498862,0.0001904053,0.000005170647,0.00007416545],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001996417,"threshold_uncertainty_score":0.01055819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01333174664682117,"score_gpt":0.2443932646412816,"score_spread":0.2310615179944605,"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."}}