{"id":"W2771030013","doi":"10.1080/24754269.2017.1400418","title":"Robust dynamic risk prediction with longitudinal studies","year":2017,"lang":"en","type":"article","venue":"Statistical Theory and Related Fields","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Human Genome Research Institute; National Institute of General Medical Sciences; National Institute of Mental Health; National Cancer Institute; National Heart, Lung, and Blood Institute; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Inference; Nonparametric statistics; Kernel (algebra); Framingham Heart Study; Econometrics; Machine learning; Data mining; Statistics; Artificial intelligence; Mathematics; Framingham Risk Score","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001016879,0.0001538435,0.0002786337,0.00002556366,0.0006705124,0.00007516784,0.0001149107,0.0001776801,0.0002884519],"category_scores_gemma":[0.008171614,0.00009731615,0.00002359943,0.00002925996,0.0007682771,0.00009801403,0.00007523415,0.0005169197,0.000009500734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001305211,"about_ca_system_score_gemma":0.0000142283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006307892,"about_ca_topic_score_gemma":0.00001016336,"domain_scores_codex":[0.9988223,0.000301938,0.0002529698,0.0002763921,0.0001389958,0.000207439],"domain_scores_gemma":[0.9949919,0.004334074,0.0001584049,0.0003340885,0.00008012387,0.0001014162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002030797,0.00003571812,0.001144367,0.00008152716,0.0001873146,0.00005711267,0.0002418814,0.000005517589,0.000001896629,0.9533214,0.000179853,0.04454033],"study_design_scores_gemma":[0.0004485824,0.0003339011,0.02177689,0.0001215336,0.0002529721,0.00004237064,0.000218378,0.002274941,0.000008779572,0.9743453,0.000037509,0.0001388887],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03214046,0.0004510982,0.9561292,0.0001650243,0.0002994352,0.0001633222,0.0002139696,0.00006902057,0.0103685],"genre_scores_gemma":[0.8820612,0.000827754,0.1162569,0.00001591501,0.00002871225,0.00001296875,0.00000439224,0.00001526551,0.0007768417],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8499208,"threshold_uncertainty_score":0.9782776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07867402560087539,"score_gpt":0.3769032291088894,"score_spread":0.298229203508014,"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."}}