{"id":"W3211038758","doi":"10.2196/19812","title":"Predicting Hepatocellular Carcinoma With Minimal Features From Electronic Health Records: Development of a Deep Learning Model","year":2021,"lang":"en","type":"article","venue":"JMIR Cancer","topic":"Hepatocellular Carcinoma Treatment and Prognosis","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Hepatocellular carcinoma; Receiver operating characteristic; Internal medicine; Medical record; Odds ratio; Cancer; Oncology; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001342697,0.0002527594,0.0005267617,0.00008105611,0.0001561751,0.00001718249,0.00007093473,0.0001097401,0.000209463],"category_scores_gemma":[0.00001509584,0.0002125175,0.0001178363,0.0002752859,0.0000353321,0.00006401593,0.00004700462,0.0003879249,0.000004367561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004944608,"about_ca_system_score_gemma":0.002361383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000663814,"about_ca_topic_score_gemma":0.001371511,"domain_scores_codex":[0.9981408,0.00007770109,0.0003868108,0.0004752253,0.000387973,0.000531454],"domain_scores_gemma":[0.9991354,0.0000394613,0.0002101783,0.0002439929,0.0001736773,0.0001972631],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003989829,0.0001557798,0.9600808,0.0002016858,0.0003055133,0.0001066086,0.004745958,0.00005297028,0.003047158,0.00001120095,0.00007130005,0.03082201],"study_design_scores_gemma":[0.007694747,0.001830076,0.5952519,0.001433447,0.0006080607,0.0001136497,0.002545868,0.2156299,0.1672873,0.00006215266,0.0066816,0.0008612925],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9803274,0.01726634,0.0008683088,0.0004444994,0.00003905829,0.0005762254,0.00001430565,0.00006825366,0.0003956484],"genre_scores_gemma":[0.988258,0.0002490366,0.009650243,0.0001849292,0.0001294504,0.0003909822,0.0002259761,0.00005515423,0.0008562118],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3648289,"threshold_uncertainty_score":0.8666213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03369052656293291,"score_gpt":0.2697382859434895,"score_spread":0.2360477593805566,"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."}}