{"id":"W2577454460","doi":"10.1016/j.canep.2017.01.001","title":"Development of a model to predict the 10-year cumulative risk of second primary cancer among cancer survivors","year":2017,"lang":"en","type":"article","venue":"Cancer Epidemiology","topic":"Multiple and Secondary Primary Cancers","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Institute of Cancer Research; Institut pour la Recherche en Santé Publique; Institut de Veille Sanitaire; Institut National Du Cancer","keywords":"Medicine; Cumulative incidence; Cancer; Poisson regression; Population; Breast cancer; Cancer registry; Prostate cancer; Cohort; Internal medicine; Colorectal cancer; Environmental health","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00204659,0.0009279007,0.0009218327,0.001067288,0.0005200513,0.001035902,0.001388292,0.0009620778,0.002631343],"category_scores_gemma":[0.003973128,0.0005856904,0.001070599,0.0004706393,0.0002031734,0.0006089706,0.0006228722,0.001120683,0.0005347563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00096201,"about_ca_system_score_gemma":0.002414299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0347232,"about_ca_topic_score_gemma":0.0195921,"domain_scores_codex":[0.9996955,0.0001241976,0.00002286932,0.00007203557,0.00003352554,0.00005191651],"domain_scores_gemma":[0.9978769,0.001582673,0.0001133142,0.00004723112,0.0002791316,0.0001007162],"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":[0.000465908,0.0006277979,0.09922285,0.00005866474,0.0005315092,0.0001931197,0.00007998112,0.8582836,0.0006715258,0.001381478,0.002885781,0.03559773],"study_design_scores_gemma":[0.00002261634,0.00004977093,0.002430961,0.000005652043,0.000042136,0.00002037041,0.00001205378,0.9967018,0.0001113092,0.0004120955,0.00018523,0.000005957258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7370378,0.0004134251,0.2507757,0.001926029,0.0002106121,0.0003396739,0.004374126,0.001528955,0.003393746],"genre_scores_gemma":[0.9449626,0.0002233363,0.04740492,0.000162451,0.00007647322,0.000439343,0.00297451,0.00005066704,0.003705515],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0347232,"threshold_uncertainty_score":0.06904215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1124488748173987,"score_gpt":0.3868792396187118,"score_spread":0.2744303648013131,"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."}}