{"id":"W4283017730","doi":"10.1186/s40635-022-00445-8","title":"Machine learning approaches to the human metabolome in sepsis identify metabolic links with survival","year":2022,"lang":"en","type":"article","venue":"Intensive Care Medicine Experimental","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; St. Michael's Hospital; Toronto General Hospital; University Health Network; University of Toronto; Ted Rogers Centre for Heart Research","funders":"National Heart, Lung, and Blood Institute; National Institutes of Health; Brigham and Women's Hospital","keywords":"Metabolome; Logistic regression; Medicine; Metabolite; Sepsis; Metabolomics; Internal medicine; Bioinformatics; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003462164,0.0002771629,0.0004601113,0.0001828055,0.0004120444,0.00001716353,0.0003615502,0.00005992065,0.0001919291],"category_scores_gemma":[0.0001984322,0.0001833593,0.00009463199,0.0003719615,0.0001739025,0.000004263429,0.0006790467,0.0005497151,0.00000361509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005502189,"about_ca_system_score_gemma":0.000028245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004929585,"about_ca_topic_score_gemma":0.0002336515,"domain_scores_codex":[0.9981878,0.0002536137,0.0002841904,0.0005427375,0.0003866967,0.0003449778],"domain_scores_gemma":[0.9992165,0.00002282897,0.0001060829,0.0003474738,0.0002179827,0.00008916145],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003633056,0.0001194378,0.03562491,0.00001560107,0.0006150008,0.00006509443,0.01946324,0.0007382868,0.9350171,0.002588421,0.004736628,0.0006529379],"study_design_scores_gemma":[0.002912718,0.002857119,0.01477673,0.00002055092,0.0001423314,0.00007715147,0.413458,0.00004660429,0.2049737,0.0000165479,0.3601347,0.0005838292],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9427329,0.05086137,0.00008926255,0.003149286,0.0005737807,0.0004806219,0.000033155,0.00001732815,0.002062317],"genre_scores_gemma":[0.9956489,0.0001294086,0.00009704605,0.002354646,0.0003656741,0.0003233794,0.0002962123,0.00004142316,0.0007432663],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7300435,"threshold_uncertainty_score":0.7477177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05054155569319907,"score_gpt":0.3035914370355893,"score_spread":0.2530498813423902,"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."}}