{"id":"W4316672979","doi":"10.1002/lci2.66","title":"Development of diagnostic and prognostic molecular biomarkers in hepatocellular carcinoma using machine learning: A systematic review","year":2022,"lang":"en","type":"review","venue":"Liver Cancer International","topic":"Cancer, Lipids, and Metabolism","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research; Vector Institute; Princess Margaret Cancer Centre; Toronto General Hospital; University Health Network; University of Toronto","funders":"Toronto General and Western Hospital Foundation","keywords":"Hepatocellular carcinoma; Medicine; Molecular biomarkers; Cochrane Library; MEDLINE; Oncology; Internal medicine; Precision medicine; Bioinformatics; Cancer; Personalized medicine; Systematic review; Meta-analysis; Pathology; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003576263,0.0003169229,0.001058634,0.0001780522,0.00004369566,0.0000151047,0.0003304568,0.0001134168,0.0001344888],"category_scores_gemma":[0.0005006655,0.000297769,0.0002333539,0.0001678114,0.00004934734,0.000004903806,0.0003091221,0.0001994731,0.000001212543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001823436,"about_ca_system_score_gemma":0.0006222789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002287652,"about_ca_topic_score_gemma":0.00006369496,"domain_scores_codex":[0.9979717,0.0002395785,0.000811901,0.0004623328,0.000326994,0.0001875531],"domain_scores_gemma":[0.9990206,0.00009330006,0.0005483685,0.0002097106,0.0000657233,0.00006228335],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002454771,0.0001256879,0.002360011,0.9381052,0.002062316,0.00001607469,0.0001804811,0.00001510928,0.0003848817,0.00004221682,0.0001892752,0.05649415],"study_design_scores_gemma":[0.0002512111,0.00002526759,0.00001967053,0.1029207,0.001186693,0.00006946105,0.00001406109,0.0002007522,0.00004090339,9.804266e-7,0.8949411,0.0003292078],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00119859,0.9955619,0.00008058571,0.000005700588,0.001507197,0.001502665,0.0001009604,0.000005023423,0.00003736967],"genre_scores_gemma":[0.0003214151,0.9972061,0.000400814,0.00005451027,0.0004106169,0.0009505565,0.0005368119,0.00004559904,0.00007358108],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8947518,"threshold_uncertainty_score":0.9999474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03741856913682609,"score_gpt":0.3035064890714514,"score_spread":0.2660879199346253,"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."}}