{"id":"W2623150809","doi":"10.1158/1538-7755.carisk16-pr17","title":"Abstract PR17: Comprehensive colorectal cancer risk prediction to inform personalized screening and intervention","year":2017,"lang":"en","type":"article","venue":"Cancer Epidemiology Biomarkers & Prevention","topic":"Nutrition, Genetics, and Disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Family history; Logistic regression; Medicine; Colorectal cancer; Receiver operating characteristic; Risk assessment; Framingham Risk Score; Oncology; Intervention (counseling); Epidemiology; Internal medicine; Cancer; Computer science; Disease","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":[0.004493712,0.001159389,0.001081055,0.001572577,0.0002996392,0.001488978,0.001102131,0.0007547516,0.006368345],"category_scores_gemma":[0.01416856,0.0004147991,0.001667178,0.001820179,0.0003208808,0.0007099725,0.001360112,0.0011327,0.001344403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008560491,"about_ca_system_score_gemma":0.002897795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009973965,"about_ca_topic_score_gemma":0.009435001,"domain_scores_codex":[0.9978583,0.001159722,0.0001157242,0.000438828,0.000325001,0.000102429],"domain_scores_gemma":[0.9948079,0.002640533,0.001097329,0.0004379442,0.0006744221,0.0003418473],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001509122,0.0003875938,0.5008665,0.001247938,0.0031255,0.0005037228,0.0001995153,0.154929,0.001650696,0.005418545,0.04115186,0.28901],"study_design_scores_gemma":[0.0005290187,0.001395014,0.1759737,0.0008240683,0.002337466,0.001061074,0.0002016776,0.7666917,0.002915558,0.02440802,0.02347546,0.0001872759],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4687817,0.01073167,0.4285892,0.01435482,0.0007913652,0.001165679,0.04934521,0.008987316,0.01725306],"genre_scores_gemma":[0.8615111,0.002171548,0.1171431,0.0008516835,0.0003002527,0.0005127186,0.01384698,0.0002808695,0.003381669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009973965,"threshold_uncertainty_score":0.02376533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04504572317567464,"score_gpt":0.371899770710768,"score_spread":0.3268540475350934,"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."}}