{"id":"W2055088902","doi":"10.1016/j.clinbiochem.2004.05.005","title":"Genomic biomarkers for cancer assessment: implementation challenges for laboratory practice","year":2004,"lang":"en","type":"review","venue":"Clinical Biochemistry","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Mount Sinai Hospital; University of Toronto","funders":"","keywords":"Genomics; Computational biology; Personalized medicine; Data science; genomic DNA; Biology; Bioinformatics; Computer science; Genetics; Genome; Gene","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":[0.01423014,0.001621771,0.003703016,0.002449801,0.0006231902,0.004888661,0.003343327,0.006231463,0.003473882],"category_scores_gemma":[0.01411454,0.0007090198,0.000924493,0.002944666,0.00319819,0.005895572,0.002004911,0.00742108,0.002306212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003090326,"about_ca_system_score_gemma":0.006417285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004879211,"about_ca_topic_score_gemma":0.00608649,"domain_scores_codex":[0.9973485,0.001102882,0.000238791,0.000357402,0.0008449486,0.0001074263],"domain_scores_gemma":[0.9732549,0.01982263,0.0006131752,0.0006489719,0.00506608,0.0005943662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000999258,0.00009832052,0.0007843708,0.003317791,0.00008600418,0.000225205,0.0001323754,0.0007124009,0.0007212702,0.02045104,0.04423307,0.9291382],"study_design_scores_gemma":[0.00007466831,0.0001961269,0.002188988,0.007541409,0.0001643495,0.001476467,0.0005056169,0.001062853,0.0008252628,0.0337963,0.9520843,0.00008361266],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00020361,0.9734858,0.006444551,0.01657291,0.001108315,0.00001818037,0.0000438076,0.00004852386,0.002074255],"genre_scores_gemma":[0.0032836,0.9725155,0.01232142,0.00809428,0.002018341,0.00007051393,0.00008346231,0.00001877882,0.001594134],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01423014,"threshold_uncertainty_score":0.07525712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08529765017468932,"score_gpt":0.5010258773694183,"score_spread":0.415728227194729,"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."}}