{"id":"W1996801243","doi":"10.1111/j.1752-8062.2012.00412.x","title":"Proteomics Improves the Prediction of Burns Mortality: Results from Regression Spline Modeling","year":2012,"lang":"en","type":"article","venue":"Clinical and Translational Science","topic":"Burn Injury Management and Outcomes","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Center for Advancing Translational Sciences; National Cancer Institute; National Center for Research Resources; National Institute of General Medical Sciences; U.S. Public Health Service","keywords":"Covariate; Medicine; Multivariate statistics; Receiver operating characteristic; Multivariate analysis; Regression analysis; Regression; Statistics; Internal medicine; Mathematics","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":[0.0251881,0.001046996,0.001444813,0.001002103,0.0003199058,0.0009248062,0.0006310882,0.0006019733,0.0005807927],"category_scores_gemma":[0.03753127,0.0003761863,0.001512248,0.001046152,0.0004763308,0.00074873,0.001013048,0.001435203,0.0002999161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004449456,"about_ca_system_score_gemma":0.001281527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006314747,"about_ca_topic_score_gemma":0.003394086,"domain_scores_codex":[0.9938135,0.005324316,0.0001528572,0.0002616652,0.0003300463,0.0001176191],"domain_scores_gemma":[0.9701962,0.02597738,0.0008769167,0.00106483,0.001475211,0.0004094393],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004345405,0.0005203255,0.1911848,0.0001961516,0.001188408,0.0001387964,0.0002811596,0.6284245,0.002442474,0.001702084,0.0014436,0.1681323],"study_design_scores_gemma":[0.00007134349,0.0004489655,0.01331035,0.00002738492,0.0001524845,0.00004065169,0.0000272369,0.9830787,0.0006167762,0.001892308,0.00030328,0.00003058086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7990034,0.00336184,0.1932369,0.001492984,0.00009592301,0.0001279029,0.0004819392,0.0007414544,0.001457667],"genre_scores_gemma":[0.9407119,0.000910549,0.05711586,0.00009251844,0.00006993123,0.00005010645,0.0004453068,0.00008536344,0.0005184727],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0251881,"threshold_uncertainty_score":0.133209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1372718453135939,"score_gpt":0.3939532288080778,"score_spread":0.2566813834944839,"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."}}