{"id":"W3098801741","doi":"10.1186/s12885-020-07618-2","title":"Development and utility assessment of a machine learning bloodstream infection classifier in pediatric patients receiving cancer treatments","year":2020,"lang":"en","type":"article","venue":"BMC Cancer","topic":"Bacterial Identification and Susceptibility Testing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; SickKids Foundation; Hospital for Sick Children","funders":"","keywords":"Medicine; Machine learning; Neutropenia; Artificial intelligence; Test set; Receiver operating characteristic; Blood test; Algorithm; False positive paradox; Internal medicine; Computer science; Chemotherapy","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":[0.000106133,0.00009127729,0.0001164736,0.00002746447,0.00004365114,0.00001344276,0.00003574677,0.00005987561,0.00004364704],"category_scores_gemma":[0.0001028674,0.0000903726,0.0000242229,0.0001100009,0.00001468654,0.000006022262,0.00005724029,0.00006871406,4.076647e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004887251,"about_ca_system_score_gemma":0.0001750795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005065038,"about_ca_topic_score_gemma":0.003868219,"domain_scores_codex":[0.9992313,0.0000673882,0.0002391999,0.0002770416,0.00008065531,0.0001043976],"domain_scores_gemma":[0.9996778,0.000009433002,0.0001260284,0.00007443775,0.00005921107,0.00005307462],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002727376,0.00008542951,0.9597568,0.00008242929,0.0000142001,6.963805e-8,0.0001541097,0.00005834397,0.02285343,6.58878e-7,0.00001398979,0.01695327],"study_design_scores_gemma":[0.0006892483,0.00005019101,0.9905989,0.00001517513,0.00002035274,6.517566e-8,0.00001220848,0.001076902,0.006225856,6.037978e-7,0.001225826,0.00008470022],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992722,0.0001987781,0.0001002965,0.00002785929,0.00008495419,0.0001618873,0.00001189113,0.000008448969,0.0001336361],"genre_scores_gemma":[0.9982401,0.0003651358,0.001070259,0.00003065947,0.00006865165,0.0000587693,0.00006944235,0.000008586499,0.00008846848],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03084207,"threshold_uncertainty_score":0.3685288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03982329149997511,"score_gpt":0.3093337351878431,"score_spread":0.269510443687868,"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."}}