{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00372528,0.0009552544,0.001429689,0.00188599,0.0006268579,0.003097811,0.001069262,0.002623994,0.01346558],"category_scores_gemma":[0.007054267,0.0002525604,0.001543443,0.001394859,0.001200569,0.002285067,0.00214554,0.003734011,0.003654195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00168203,"about_ca_system_score_gemma":0.003589652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002525618,"about_ca_topic_score_gemma":0.002649062,"domain_scores_codex":[0.9979254,0.0008451667,0.0001711361,0.0003733128,0.0005523677,0.0001325318],"domain_scores_gemma":[0.996183,0.002153678,0.0003664416,0.0002360591,0.0008256086,0.0002351853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002767414,0.0001208492,0.001448458,0.01273437,0.0004425403,0.0001984926,0.0002714967,0.0006336833,0.001585206,0.04861287,0.06061958,0.8730557],"study_design_scores_gemma":[0.00007959661,0.000213976,0.003022043,0.01704133,0.0004165623,0.0005970389,0.0001841852,0.0005290899,0.001065076,0.05133403,0.9254519,0.00006507245],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.001275574,0.9448841,0.007786886,0.02568441,0.003986752,0.00006522089,0.0005301734,0.0002758801,0.01551107],"genre_scores_gemma":[0.02364293,0.9452174,0.01307243,0.009241045,0.003621549,0.000143403,0.0005960603,0.00006713789,0.004398052],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01346558,"threshold_uncertainty_score":0.04504687,"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."}}