{"id":"W2948548347","doi":"10.1101/662106","title":"A new colorectal cancer risk prediction model incorporating family history, personal and environmental factors","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; University of Toronto; Mount Sinai Hospital","funders":"National Health and Medical Research Council; National Cancer Institute; Medical Research Council; National Institutes of Health","keywords":"Family history; Medicine; Colorectal cancer; Confidence interval; Population; Logistic regression; Demography; Internal medicine; Cancer registry; Incidence (geometry); Cancer; Oncology; 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":[0.003028481,0.0009439065,0.001132703,0.001385436,0.0004395973,0.001250348,0.001127754,0.0007374483,0.001510096],"category_scores_gemma":[0.00463529,0.0004246494,0.001202477,0.0006958072,0.0002679141,0.0006216466,0.0006254719,0.001131791,0.0003886381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001099435,"about_ca_system_score_gemma":0.001849485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02251466,"about_ca_topic_score_gemma":0.01232045,"domain_scores_codex":[0.9992548,0.0002733344,0.00004484962,0.0002385047,0.0001111558,0.0000773887],"domain_scores_gemma":[0.9976503,0.001474044,0.0002110535,0.00007465701,0.0004774739,0.0001123645],"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":[0.0008256398,0.000583064,0.1144887,0.0000972163,0.0008012326,0.0004147415,0.0001287872,0.7968317,0.001177365,0.001390296,0.004024867,0.07923631],"study_design_scores_gemma":[0.00002872102,0.00005496598,0.003178843,0.00001077698,0.00007400199,0.00006613396,0.000007733469,0.9958059,0.00008516489,0.0005015092,0.0001759134,0.00001037694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6725105,0.001190631,0.3161908,0.002154926,0.0002222787,0.0002845557,0.002547618,0.001401196,0.003497546],"genre_scores_gemma":[0.962397,0.000239884,0.03344603,0.0001437593,0.00007907872,0.0002009675,0.001207552,0.0000375425,0.002248284],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02251466,"threshold_uncertainty_score":0.0447672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01659257862292775,"score_gpt":0.2102296144258495,"score_spread":0.1936370358029218,"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."}}