{"id":"W2947562522","doi":"10.1158/1055-9965.epi-18-1291","title":"Urinary Metabolomics to Identify a Unique Biomarker Panel for Detecting Colorectal Cancer: A Multicenter Study","year":2019,"lang":"en","type":"article","venue":"Cancer Epidemiology Biomarkers & Prevention","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; The Metabolomics Innovation Centre","funders":"National Institute of Biomedical Imaging and Bioengineering; National Cancer Institute; National Institutes of Health; Mitacs","keywords":"Colorectal cancer; Biomarker; Urinary system; Metabolomics; Medicine; Multicenter study; Oncology; Cancer; Internal medicine; Bioinformatics; Biology; Randomized controlled trial; Genetics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.002459201,0.0004141469,0.0007833245,0.0002226303,0.0002043093,0.00001977211,0.0003645206,0.0003096064,0.00008432114],"category_scores_gemma":[0.0007113517,0.000393421,0.0004034742,0.0002795119,0.00009233376,0.00001687692,0.00036296,0.0001524722,0.000016172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001381464,"about_ca_system_score_gemma":0.0001316026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003057858,"about_ca_topic_score_gemma":0.0006717762,"domain_scores_codex":[0.9964543,0.0007195749,0.0007960489,0.001173691,0.0001081606,0.0007482462],"domain_scores_gemma":[0.9984281,0.000242764,0.0004600946,0.0004776181,0.0002170331,0.0001743625],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.002335201,0.0003435297,0.4428106,0.00008017621,0.00231733,0.000001679959,0.0001302659,0.00006591398,0.5387313,0.00004778967,0.002781031,0.01035522],"study_design_scores_gemma":[0.005683262,0.003064555,0.900699,0.0001356074,0.0007753032,0.00001152754,0.001293348,0.0009742019,0.05552141,0.0008011366,0.02995076,0.001089903],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9785446,0.008666783,0.006277252,0.0008555942,0.002010453,0.003438238,0.0001072139,0.00004049247,0.00005935263],"genre_scores_gemma":[0.9896029,0.001524293,0.004613992,0.0005781008,0.0002865239,0.002460937,0.0001210953,0.000064158,0.0007479698],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4832099,"threshold_uncertainty_score":0.9998518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06183883944335727,"score_gpt":0.3924476562637359,"score_spread":0.3306088168203786,"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."}}