{"id":"W2999965957","doi":"10.1158/1055-9965.epi-19-0929","title":"A New Comprehensive Colorectal Cancer Risk Prediction Model Incorporating Family History, Personal Characteristics, and Environmental Factors","year":2020,"lang":"en","type":"article","venue":"Cancer Epidemiology Biomarkers & Prevention","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute","funders":"National Institute of Environmental Health Sciences; National Cancer Institute; National Institutes of Health; Cancer Research UK","keywords":"Medicine; Colorectal cancer; Family history; Population; Internal medicine; Confidence interval; Cancer registry; Cancer; Oncology; Demography; Gynecology; Environmental health","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002930842,0.0003048615,0.000624433,0.00009453228,0.0001617653,0.000006705424,0.00006111591,0.0002833055,0.0001355353],"category_scores_gemma":[0.0002018037,0.0002981703,0.0002004573,0.0000971901,0.0002365775,0.0001564681,0.00006793527,0.00042267,0.000003329652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001060094,"about_ca_system_score_gemma":0.0003649071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001615908,"about_ca_topic_score_gemma":0.0001230585,"domain_scores_codex":[0.9979432,0.0003200131,0.0005813973,0.0006940166,0.0001447401,0.000316697],"domain_scores_gemma":[0.9986672,0.0001749818,0.0005964797,0.0001032469,0.00004423006,0.00041381],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00567265,0.00003944445,0.8935899,0.00009836805,0.0005816075,0.000004099762,0.001184134,0.0001726222,0.0307231,0.000008798288,0.009714297,0.05821097],"study_design_scores_gemma":[0.001578846,0.001446101,0.763456,0.0001500895,0.0005113043,0.00001291821,0.0004172554,0.2301265,0.0003573626,0.0001795786,0.001507372,0.000256737],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9636067,0.01256131,0.02089576,0.001109729,0.0007846183,0.0005889062,0.0002614325,0.0001511726,0.00004042685],"genre_scores_gemma":[0.993504,0.002123312,0.002232866,0.0008159435,0.0005477765,0.0001178158,0.0004668402,0.00003594091,0.0001554423],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2299538,"threshold_uncertainty_score":0.9999471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06522830338400573,"score_gpt":0.2984089056808391,"score_spread":0.2331806022968334,"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."}}