{"id":"W4286239345","doi":"10.3233/cbm-220034","title":"Identification of urinary biomarkers of colorectal cancer: Towards the development of a colorectal screening test in limited resource settings","year":2022,"lang":"en","type":"article","venue":"Cancer Biomarkers","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; National Cancer Institute; Genome Canada","keywords":"Medicine; Colonoscopy; Colorectal cancer; Logistic regression; Urine; Metabolite; Urinary system; Receiver operating characteristic; Internal medicine; Oncology; Biomarker; Cancer; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003768901,0.0006347247,0.0009623537,0.001665874,0.0002724887,0.001753176,0.0005691739,0.0007996888,0.001334957],"category_scores_gemma":[0.01096461,0.0002659284,0.0003375447,0.001353431,0.0006060503,0.001149154,0.0006544936,0.0007076346,0.0004432787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007635076,"about_ca_system_score_gemma":0.0024103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002232934,"about_ca_topic_score_gemma":0.002402812,"domain_scores_codex":[0.9986722,0.0007515646,0.0001047492,0.000143532,0.0002533808,0.00007455138],"domain_scores_gemma":[0.993681,0.002709703,0.00172604,0.000230205,0.001227099,0.0004260487],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008620719,0.0005780241,0.7146144,0.001542924,0.0002688485,0.0003611998,0.0001841004,0.003863774,0.02981205,0.001879601,0.004051866,0.2419812],"study_design_scores_gemma":[0.0002416294,0.005520789,0.7869196,0.003887785,0.0009478912,0.002984907,0.002402834,0.05274142,0.07375176,0.01137428,0.05904941,0.00017776],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7679709,0.07175201,0.1210644,0.02688614,0.0004761159,0.0007945643,0.003823959,0.0007103109,0.006521675],"genre_scores_gemma":[0.8379795,0.01247992,0.1456688,0.001286027,0.0002249146,0.0002650839,0.0009851222,0.00003472607,0.001076013],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003768901,"threshold_uncertainty_score":0.01993209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01449670586261191,"score_gpt":0.2622463053505061,"score_spread":0.2477495994878942,"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."}}