{"id":"W2564095444","doi":"10.1038/srep38707","title":"Lipidomic analysis enables prediction of clinical outcomes in burn patients","year":2016,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Burn Injury Management and Outcomes","field":"Medicine","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre; Sunnybrook Hospital; University of Toronto","funders":"National Institute of General Medical Sciences; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Hypermetabolism; Medicine; Sepsis; Internal medicine; Burn injury; Prospective cohort study; Lipid metabolism; Inflammation; Cohort; Burn center; Lipid profile; Cohort study; Inflammatory response; Physiology; Gastroenterology; Surgery; Poison control; Emergency medicine; Cholesterol","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002355463,0.0001122199,0.0006216513,0.0007454375,0.00004589748,0.00002510438,0.00007820454,0.00008087293,0.0003333747],"category_scores_gemma":[0.0009546957,0.00006491353,0.0004063488,0.0008982383,0.0002089357,0.0001709605,0.00007212793,0.00006855181,0.00001833313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004213776,"about_ca_system_score_gemma":0.00006263143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003498109,"about_ca_topic_score_gemma":0.00004329544,"domain_scores_codex":[0.9971998,0.00005845892,0.001405797,0.0005454561,0.0005705791,0.0002198939],"domain_scores_gemma":[0.9983053,0.000109039,0.0005147352,0.0007893466,0.0001826709,0.00009897468],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004076435,0.0002957985,0.9898585,0.00002060851,0.0002794942,0.00003427419,0.00003923719,0.000002093335,0.0003126164,0.0000194144,0.003978868,0.005118352],"study_design_scores_gemma":[0.0009770476,0.00007193201,0.9893916,0.00005099524,0.0003797723,0.000001647235,0.00002195333,0.00002797345,0.0005852217,0.0004894632,0.007939715,0.00006266671],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925106,0.00001728923,0.0001396112,0.0002685522,0.005147038,0.0003184326,0.000006566135,0.00003614273,0.001555759],"genre_scores_gemma":[0.9841983,0.00001238281,0.0001656401,0.00003299929,0.00005691705,0.000008322598,0.00004391352,0.000007812525,0.01547373],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01391797,"threshold_uncertainty_score":0.3650219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03349341897234469,"score_gpt":0.3271070597957847,"score_spread":0.29361364082344,"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."}}