{"id":"W3018700298","doi":"10.1158/1055-9965.epi-19-1389","title":"Genetic and Circulating Biomarker Data Improve Risk Prediction for Pancreatic Cancer in the General Population","year":2020,"lang":"en","type":"article","venue":"Cancer Epidemiology Biomarkers & Prevention","topic":"Pancreatic and Hepatic Oncology Research","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Cancer Institute; National Heart, Lung, and Blood Institute; National Institutes of Health","keywords":"Medicine; Pancreatic cancer; Population; Internal medicine; Oncology; Disease; Cancer; Biomarker; Prospective cohort study; Absolute risk reduction; Family history; Confidence interval; Environmental health; Biology; Genetics","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":[0.0109184,0.0009634555,0.0007684365,0.001483736,0.000304334,0.001553305,0.0006396421,0.0007422221,0.001384779],"category_scores_gemma":[0.02535388,0.0003718476,0.001376153,0.0009366607,0.000358751,0.001064497,0.001088162,0.001445398,0.0003740965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005040923,"about_ca_system_score_gemma":0.0009913566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004687559,"about_ca_topic_score_gemma":0.00492879,"domain_scores_codex":[0.9969882,0.001841167,0.0001504103,0.0005816234,0.0003201465,0.0001183907],"domain_scores_gemma":[0.9882388,0.008313019,0.001109456,0.001165682,0.0007343857,0.000438501],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006305419,0.0002950229,0.946312,0.00005476017,0.001136275,0.00006292528,0.0001132472,0.02504346,0.000396267,0.0002742514,0.0008146444,0.02486661],"study_design_scores_gemma":[0.0001625637,0.001693914,0.5564799,0.0001301825,0.001732552,0.0006071525,0.0002235313,0.4299797,0.001268438,0.005666445,0.001990235,0.00006533515],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9790142,0.001254158,0.01690658,0.0006839682,0.00004145248,0.00004308403,0.000787718,0.0001536693,0.001115131],"genre_scores_gemma":[0.9941671,0.0001805737,0.004571631,0.000140822,0.00003094122,0.00001327976,0.0006518855,0.00001551472,0.0002282221],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0109184,"threshold_uncertainty_score":0.05774266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1576432534179122,"score_gpt":0.443807844807674,"score_spread":0.2861645913897618,"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."}}