{"id":"W4409745923","doi":"10.1093/eurjpc/zwaf254","title":"Metabolomics data improve 10-year cardiovascular risk prediction with the SCORE2 algorithm for the general population without cardiovascular disease or diabetes","year":2025,"lang":"en","type":"article","venue":"European Journal of Preventive Cardiology","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Bundesministerium für Familie, Senioren, Frauen und Jugend; Medical Research Council Canada; China Scholarship Council; Ministerium für Soziales, Gesundheit, Frauen und Familie, Saarland; Northwest Regional Development Agency; Bundesministerium für Bildung und Forschung; British Heart Foundation; Wellcome Trust","keywords":"Medicine; Mace; Metabolomics; Population; Biomarker; Internal medicine; Diabetes mellitus; Disease; Bioinformatics; Endocrinology; Myocardial infarction; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007095484,0.001464871,0.001273644,0.000925802,0.0003732683,0.00129395,0.0007825419,0.0008466605,0.001491411],"category_scores_gemma":[0.01296035,0.0003113007,0.001612216,0.0005571189,0.0003131474,0.0007170489,0.001442189,0.001267091,0.0006111067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003777405,"about_ca_system_score_gemma":0.001060981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002102562,"about_ca_topic_score_gemma":0.001911186,"domain_scores_codex":[0.9977611,0.001530197,0.00007823393,0.0003367328,0.000195969,0.00009762721],"domain_scores_gemma":[0.9959842,0.002842059,0.000349107,0.0002756038,0.0003926939,0.0001564358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.004607277,0.0007719235,0.2993885,0.0003620016,0.003016168,0.0005320961,0.0002374769,0.4721875,0.004224527,0.002161963,0.01224268,0.2002679],"study_design_scores_gemma":[0.0001671187,0.0004963377,0.01981242,0.00005399906,0.0002020169,0.0001874372,0.00003689221,0.9738324,0.000807512,0.002871953,0.001494264,0.00003754887],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7060941,0.002664528,0.282282,0.00226767,0.000261271,0.000177255,0.002159773,0.001193819,0.00289962],"genre_scores_gemma":[0.9444827,0.000454712,0.04984222,0.0005490956,0.0001591506,0.0001386446,0.00295174,0.0001287455,0.001293048],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007095484,"threshold_uncertainty_score":0.03752494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01409172848284499,"score_gpt":0.2457876522532732,"score_spread":0.2316959237704282,"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."}}