{"id":"W2162863739","doi":"10.1186/cc11667","title":"Identification of sepsis subtypes in critically ill adults using gene expression profiling","year":2012,"lang":"en","type":"article","venue":"Critical Care","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research","keywords":"Medicine; Critically ill; Sepsis; Gene expression profiling; Profiling (computer programming); Intensive care medicine; Severe sepsis; Identification (biology); Bioinformatics; Gene; Computational biology; Gene expression; Internal medicine; Septic shock; Genetics; 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":[0.0004476473,0.0002322624,0.0004261857,0.000885297,0.0001949244,0.000367916,0.0001498337,0.000287286,0.0005406066],"category_scores_gemma":[0.001432812,0.0000878808,0.0002890322,0.0007972684,0.0001730042,0.0001659744,0.0002977074,0.0002445709,0.0001818753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002251259,"about_ca_system_score_gemma":0.0002075866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008138073,"about_ca_topic_score_gemma":0.0009231296,"domain_scores_codex":[0.9996866,0.00008274787,0.00003720161,0.0000957189,0.0000599485,0.00003778709],"domain_scores_gemma":[0.9994667,0.0001737333,0.0001726414,0.00004343097,0.00008368889,0.00005985414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001442557,0.0001729961,0.9085296,0.0001444203,0.000115963,0.0001850443,0.0003254186,0.002356343,0.0346863,0.0001260797,0.0005459165,0.05136931],"study_design_scores_gemma":[0.00003179368,0.0006735785,0.9765539,0.00003791093,0.00006311927,0.0006020961,0.0004533776,0.01263615,0.007384554,0.0007474188,0.0007952682,0.00002070986],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950829,0.0003133125,0.003355093,0.00009838517,0.000005036727,0.00006074702,0.0007971582,0.00001891568,0.0002685358],"genre_scores_gemma":[0.9912739,0.0001586075,0.00683006,0.00005442782,0.000009307249,0.0000642892,0.001483783,0.000003672699,0.0001218779],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000885297,"threshold_uncertainty_score":0.002367377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08264240219981202,"score_gpt":0.3916860741802507,"score_spread":0.3090436719804387,"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."}}