{"id":"W2748190412","doi":"10.1093/cid/cix775","title":"Optimizing Empiric Antibiotic Selection in Sepsis: Turning Probabilities Into Practice","year":2017,"lang":"en","type":"letter","venue":"Clinical Infectious Diseases","topic":"Bacterial Identification and Susceptibility Testing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Medicine; Sepsis; Selection (genetic algorithm); Intensive care medicine; Antibiotics; Internal medicine; Microbiology; Machine learning; Computer science","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":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008372093,0.0003809579,0.0005655614,0.000140394,0.0003391538,0.0004281146,0.0003735558,0.001116149,0.00003773669],"category_scores_gemma":[0.01818055,0.0004009799,0.0003796189,0.0001282832,0.000320121,0.00004046658,0.0002268717,0.001241905,0.00004746002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001142182,"about_ca_system_score_gemma":0.0004577359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001778689,"about_ca_topic_score_gemma":0.0002501134,"domain_scores_codex":[0.996347,0.0009550648,0.0009652193,0.001113022,0.000204276,0.0004154103],"domain_scores_gemma":[0.9974202,0.0005364428,0.0008049048,0.000736984,0.0003776024,0.0001238752],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001329302,0.001194047,0.3964125,0.001197604,0.00046088,0.0001309137,0.0002071567,0.0001982061,0.006075835,0.000008694726,0.5875322,0.006449082],"study_design_scores_gemma":[0.002003877,0.001307316,0.1230372,0.0008683855,0.0007439252,0.00006481279,0.0002015664,0.0003232903,0.0005768285,0.0008415116,0.8681593,0.001871978],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8837314,0.001991027,0.0003138723,0.1052847,0.004015301,0.001712871,0.00007823684,0.0003833599,0.002489236],"genre_scores_gemma":[0.8695289,0.0005066423,0.0006627826,0.1163966,0.00971789,0.0001017085,0.001259107,0.0001405366,0.001685758],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2806271,"threshold_uncertainty_score":0.9998442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04100201286941735,"score_gpt":0.3754208071093416,"score_spread":0.3344187942399243,"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."}}