{"id":"W4318574704","doi":"10.1159/000529054","title":"Precision Nutrition for Cardiovascular Disease Prevention","year":2023,"lang":"en","type":"article","venue":"Lifestyle Genomics","topic":"Nutrition, Genetics, and Disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Université Laval","keywords":"Medicine; Nutrigenomics; Psychological intervention; Disease; Precision medicine; Personalized medicine; Population; Epigenome; Microbiome; Intensive care medicine; Environmental health; Risk analysis (engineering); Bioinformatics; Biology; Pathology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002531534,0.0001394176,0.0001336308,0.00006835032,0.0001371123,0.00003485089,0.0001419835,0.0001069799,0.000005720536],"category_scores_gemma":[0.0001265916,0.0001621749,0.0004325583,0.0001032058,0.00003487123,0.000005051339,0.00007496997,0.00003190953,0.00004286136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000220604,"about_ca_system_score_gemma":0.0001032138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002350199,"about_ca_topic_score_gemma":0.000002821444,"domain_scores_codex":[0.9989207,0.00005398381,0.0001993532,0.000427339,0.0001416408,0.0002569422],"domain_scores_gemma":[0.9991094,0.0000145468,0.00004610541,0.0005228821,0.0001075863,0.0001994953],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.005763454,0.002976242,0.009318354,0.002557853,0.002576338,0.00005406708,0.0002946797,0.02270705,0.6397781,0.001619629,0.2308085,0.08154575],"study_design_scores_gemma":[0.003732475,0.0004729693,0.005920394,0.00005749241,0.0004001581,0.000005310976,0.0001215772,0.001248421,0.02772413,0.01134488,0.9484271,0.0005450909],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9727719,0.006528936,0.018312,0.0001719284,0.0005080267,0.001178595,0.0003603204,0.00007531687,0.00009291832],"genre_scores_gemma":[0.9738908,0.00838714,0.00462211,0.0002286143,0.003155012,0.0008221032,0.006500715,0.00009718659,0.002296249],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7176186,"threshold_uncertainty_score":0.66133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01639362420527957,"score_gpt":0.2640320689336791,"score_spread":0.2476384447283995,"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."}}