{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003126902,0.0005082951,0.0006429648,0.001118911,0.0002409767,0.000782655,0.0007319371,0.000719642,0.0006917373],"category_scores_gemma":[0.007556486,0.0001662761,0.0004377551,0.0005055043,0.0001689357,0.000612095,0.0004375723,0.0007582826,0.0002678003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008811543,"about_ca_system_score_gemma":0.001199178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003065123,"about_ca_topic_score_gemma":0.001942076,"domain_scores_codex":[0.9991477,0.0003185991,0.0001047593,0.0001584028,0.0001856994,0.00008482824],"domain_scores_gemma":[0.9962878,0.001977451,0.0003483961,0.0001397923,0.001072213,0.0001744044],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001022393,0.0008204034,0.6909874,0.0000899114,0.0002419908,0.0002326796,0.0001001632,0.1022609,0.002626351,0.0003149737,0.002176074,0.1991267],"study_design_scores_gemma":[0.00005620536,0.0009024895,0.03655079,0.00003428563,0.00007259625,0.0002459216,0.00008871061,0.9580204,0.003084138,0.0003213794,0.0006095896,0.00001347432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9546726,0.0004868413,0.04201926,0.0005182894,0.00006282258,0.0002275775,0.0005691638,0.0003388916,0.001104587],"genre_scores_gemma":[0.9656272,0.0001362664,0.03313623,0.0000928189,0.00003204461,0.0001250657,0.0006176899,0.0000099115,0.0002228991],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003126902,"threshold_uncertainty_score":0.01653683,"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."}}