{"id":"W3093041399","doi":"10.3390/jpm10040167","title":"Colon Cancer Biomarkers: Implications for Personalized Medicine","year":2020,"lang":"en","type":"editorial","venue":"Journal of Personalized Medicine","topic":"Colorectal Cancer Treatments and Studies","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Personalized medicine; Precision medicine; Medicine; Biomarker; Colorectal cancer; Biomarker discovery; Clinical Practice; Bioinformatics; Cancer; Internal medicine; Pathology; Proteomics; Biology","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":[0.009424468,0.003275452,0.002950275,0.002984111,0.001731546,0.007117883,0.002910657,0.01146041,0.005981189],"category_scores_gemma":[0.02442158,0.000819986,0.0023199,0.001147726,0.003327186,0.005247325,0.001411163,0.02052622,0.004972221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002717091,"about_ca_system_score_gemma":0.002522632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008998159,"about_ca_topic_score_gemma":0.001748149,"domain_scores_codex":[0.9963202,0.0009451879,0.0005717368,0.0004100442,0.001583595,0.0001691923],"domain_scores_gemma":[0.9704503,0.01570246,0.001188363,0.0006755005,0.009340581,0.002642933],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000096983,0.00001951723,0.00005768712,0.0006906606,0.00003375347,0.0001851012,0.00002944544,0.0000594546,0.0001486835,0.001401355,0.9758514,0.02142596],"study_design_scores_gemma":[0.00005991734,0.00006500809,0.0002920343,0.0006800593,0.00007021687,0.0005419028,0.00007536542,0.0002752951,0.0001491786,0.004158992,0.9936005,0.00003141806],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.00005196819,0.02377914,0.0002815371,0.0507801,0.924265,0.00001500301,0.00003423327,0.00004887503,0.0007441018],"genre_scores_gemma":[0.0004286887,0.0166334,0.000265555,0.01890932,0.9600585,0.00001389801,0.00001940169,0.00001693557,0.0036542],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.01146041,"threshold_uncertainty_score":0.04984194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05191863904823283,"score_gpt":0.3995478968642424,"score_spread":0.3476292578160096,"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."}}