{"id":"W3204058705","doi":"10.2196/21074","title":"Can artificial Intelligence Support Clinical Decision Making in the Management of Hepatocellular Carcinoma Patients? (Preprint)","year":2020,"lang":"en","type":"article","venue":"JMIR Cancer","topic":"Hepatocellular Carcinoma Treatment and Prognosis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Preprint; Hepatocellular carcinoma; Medicine; Computer science; Internal medicine; World Wide Web","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":[0.0003727861,0.0001723033,0.0003583312,0.00006259086,0.0000390431,0.00001643433,0.0002249785,0.00009326626,0.0005148001],"category_scores_gemma":[0.00002520233,0.000123932,0.0002153588,0.0004010247,0.00007196196,0.00003849187,0.0001113651,0.0002387039,0.00004392694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000860991,"about_ca_system_score_gemma":0.00008678004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007719682,"about_ca_topic_score_gemma":0.00003283001,"domain_scores_codex":[0.9980473,0.00009472786,0.000734847,0.0004159998,0.0004548451,0.0002523105],"domain_scores_gemma":[0.9992069,0.00009094819,0.0001494198,0.0003627377,0.00008019243,0.000109813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004422923,0.000313071,0.8179964,0.0001531442,0.00005781696,0.000286801,0.001452619,0.000003494653,0.00001403367,0.0003243018,0.0002371995,0.1787188],"study_design_scores_gemma":[0.0009537978,0.0009159728,0.9849917,0.0003792892,0.0002401007,0.00000369294,0.0009108052,0.006488559,0.001751615,0.0007621997,0.002383449,0.0002187935],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945424,0.0001835012,0.0003511738,0.002280378,0.0001298705,0.001687072,0.00001567015,0.00002130434,0.0007886029],"genre_scores_gemma":[0.9976923,0.0001247027,0.0006959728,0.0009865784,0.0001369022,0.0003059685,0.00002327606,0.00002032742,0.00001391497],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1785,"threshold_uncertainty_score":0.56367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1325436933912452,"score_gpt":0.3607192703944718,"score_spread":0.2281755770032266,"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."}}