{"id":"W6976767981","doi":"10.6084/m9.figshare.13237437.v1","title":"Additional file 1 of Development and utility assessment of a machine learning bloodstream infection classifier in pediatric patients receiving cancer treatments","year":2020,"lang":"en","type":"article","venue":"Figshare","topic":"Bacterial Identification and Susceptibility Testing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; SickKids Foundation; Hospital for Sick Children","funders":"","keywords":"Cancer; Classifier (UML); Bloodstream infection; MEDLINE; Risk assessment","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00001991258,0.00006301813,0.00008535704,0.0000227713,0.00002611798,0.000006216869,0.00003060068,0.00005082495,0.4011308],"category_scores_gemma":[0.001324388,0.00006501026,0.00002143171,0.00008324822,0.000004125337,0.000005060567,0.00005770242,0.00005240886,0.000004373751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001455612,"about_ca_system_score_gemma":0.0001194349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001503061,"about_ca_topic_score_gemma":0.0001043448,"domain_scores_codex":[0.9994609,0.00004213538,0.0001913628,0.0001739453,0.00006917176,0.00006247199],"domain_scores_gemma":[0.9996183,0.0000581498,0.0001496183,0.00005114415,0.0000883549,0.00003444781],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002461198,0.0002964629,0.7055113,0.0003878989,0.00003670566,3.338928e-7,0.0001277001,0.00002123628,0.002961687,9.516922e-8,0.2668628,0.02376927],"study_design_scores_gemma":[0.0002305068,0.00006134846,0.9621318,0.0001315349,0.000004485945,8.633643e-8,0.000004307489,0.0004211397,0.001486444,2.579173e-7,0.03547282,0.00005526081],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.4158056,0.00001498867,4.137999e-7,0.000004847655,0.000006963608,0.00009090728,0.5838515,0.000004288996,0.0002204564],"genre_scores_gemma":[0.550925,0.000003732235,0.0003133613,0.000007836057,0.00001961254,0.0001093931,0.4485756,0.000003569866,0.00004190795],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.4011264,"threshold_uncertainty_score":0.5994167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03409762423737711,"score_gpt":0.2719186617880457,"score_spread":0.2378210375506686,"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."}}