{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00142298,0.0007445684,0.0009633004,0.0008556765,0.0003916622,0.0007571821,0.0009292663,0.0006032216,0.00144049],"category_scores_gemma":[0.002207074,0.0003420307,0.0008868906,0.0005286673,0.0002125592,0.0004321826,0.0005351232,0.00070577,0.0002842736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009404186,"about_ca_system_score_gemma":0.00194031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03195043,"about_ca_topic_score_gemma":0.02384499,"domain_scores_codex":[0.9995659,0.0001196724,0.00002210679,0.0001682639,0.00007204066,0.00005192963],"domain_scores_gemma":[0.999129,0.0004534334,0.0001039021,0.00003558341,0.0002084937,0.00006974983],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003954903,0.0003158324,0.05108384,0.00005574133,0.0005002182,0.0002863745,0.00007111069,0.8848394,0.001219039,0.0009676685,0.002744544,0.05752081],"study_design_scores_gemma":[0.00002226873,0.00004722314,0.00285841,0.000006566123,0.00005235566,0.00004961579,0.00000454855,0.9963627,0.00007260225,0.0003487371,0.0001678815,0.000007141257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6085048,0.0009083739,0.3813206,0.001514142,0.000107017,0.0002419891,0.002809006,0.001175054,0.003418991],"genre_scores_gemma":[0.9591652,0.0002058461,0.03568626,0.0001631184,0.00005493647,0.0001869867,0.001512407,0.00002968519,0.002995588],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03195043,"threshold_uncertainty_score":0.0635289,"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."}}