{"id":"W2901777987","doi":"10.1373/clinchem.2018.291922","title":"Development and Validation of Apolipoprotein AI-Associated Lipoprotein Proteome Panel for the Prediction of Cholesterol Efflux Capacity and Coronary Artery Disease","year":2018,"lang":"en","type":"article","venue":"Clinical Chemistry","topic":"Cardiovascular Disease and Adiposity","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Prevention of Organ Failure","funders":"Medical Research Council; University College London Hospitals NHS Foundation Trust; National Institute for Health and Care Research; Cancer Research UK","keywords":"Lipoprotein; Apolipoprotein B; Proteome; Cholesterol; Coronary artery disease; Internal medicine; High-density lipoprotein; Multivariate statistics; Medicine; Biology; Computer science; Bioinformatics; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"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":[0.003957634,0.0006279423,0.000581316,0.00176185,0.0004334736,0.0008376697,0.0005182872,0.0007003669,0.0005019581],"category_scores_gemma":[0.004124999,0.0002832591,0.0004380786,0.000794459,0.0003371252,0.0002984301,0.0006450766,0.0006172968,0.0004386619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003464767,"about_ca_system_score_gemma":0.0005959456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001048179,"about_ca_topic_score_gemma":0.0009286627,"domain_scores_codex":[0.9983875,0.0005726125,0.000110033,0.000401763,0.0004605709,0.00006754138],"domain_scores_gemma":[0.9984428,0.0005188205,0.0001964848,0.0001718284,0.0005089628,0.0001611158],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001715382,0.001152554,0.4159899,0.0001463224,0.000584733,0.0003432032,0.000191793,0.006024613,0.5243471,0.0003212356,0.0009381651,0.04824505],"study_design_scores_gemma":[0.0001771626,0.002577409,0.5837948,0.00005151655,0.0004504202,0.002113979,0.0001749654,0.2337656,0.1740243,0.000554224,0.002239826,0.00007573172],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9729261,0.0004785472,0.02441046,0.0001400524,0.00002434146,0.0002140197,0.00121265,0.0001996598,0.0003942427],"genre_scores_gemma":[0.9593157,0.0001322491,0.03824317,0.0001065625,0.00001691985,0.000147717,0.001778357,0.00001953832,0.0002397551],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003957634,"threshold_uncertainty_score":0.02093017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05988705808665441,"score_gpt":0.2977249144836404,"score_spread":0.237837856396986,"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."}}