{"id":"W3198964163","doi":"10.23958/ijirms/vol06-i09/1170","title":"Genetics - Predisposition and Application to Primary Preventions of CAD","year":2021,"lang":"en","type":"article","venue":"International Journal of Innovative Research in Medical Science","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research","keywords":"Coronary artery disease; Genetic predisposition; Medicine; Risk assessment; Internal medicine; Disease; Framingham Risk Score; CAD; Prospective cohort study; Bioinformatics; Biology","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":[0.004144957,0.00003632518,0.00009251949,0.0002552029,0.00003952743,0.00001285808,0.0003643176,0.00005935857,0.00001206625],"category_scores_gemma":[0.003307006,0.00003215259,0.00001772547,0.00088161,0.0004105211,0.000009268158,0.0002750046,0.0001738126,6.885524e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007601003,"about_ca_system_score_gemma":0.0009871471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001051096,"about_ca_topic_score_gemma":0.0000196342,"domain_scores_codex":[0.9982139,0.0001577505,0.000380502,0.0001483117,0.0009574926,0.0001419706],"domain_scores_gemma":[0.9962227,0.00008909134,0.0001309649,0.00008142834,0.003374072,0.0001017398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00005306001,0.0002128968,0.1389045,0.00001015421,0.00003570336,0.00001681895,0.0001308408,0.0001784252,0.7721782,0.001961341,0.0005279731,0.08579011],"study_design_scores_gemma":[0.0004610979,0.0003287263,0.9382588,0.0001164374,0.000001321671,0.0001077786,0.0002230598,0.0003662693,0.05458556,0.002972071,0.0025205,0.0000584314],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9627343,0.0002469414,0.03156569,0.004640546,0.0001117698,0.00006851446,0.000007811356,4.391642e-7,0.0006239231],"genre_scores_gemma":[0.9932566,0.0004044429,0.005926374,0.000232822,0.000131831,0.000006179452,0.00001064619,0.000002149231,0.00002897869],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7993543,"threshold_uncertainty_score":0.3959034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03601708042250641,"score_gpt":0.4525917777755319,"score_spread":0.4165746973530255,"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."}}