{"id":"W2968364773","doi":"10.1136/sextrans-2019-sti.71","title":"S15.3 Reducing the global burden of infectious diseases through precision infection management (PIM)","year":2019,"lang":"en","type":"article","venue":"","topic":"Bacterial Identification and Susceptibility Testing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Workflow; Informatics; Medicine; Psychological intervention; Omics; Clinical microbiology; Health informatics tools; Health informatics; Data science; Bioinformatics; Computer science; Public health; Biology; Database; Microbiology; Pathology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002758991,0.0009430701,0.001036404,0.001227688,0.001212747,0.003884602,0.002390384,0.003120031,0.2706158],"category_scores_gemma":[0.007624849,0.0003930551,0.001218665,0.001419565,0.0006104853,0.001549117,0.002633606,0.001422669,0.08736134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003140633,"about_ca_system_score_gemma":0.009358418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009681502,"about_ca_topic_score_gemma":0.02582831,"domain_scores_codex":[0.9979672,0.0003162315,0.00009810942,0.0001616302,0.001149422,0.0003073442],"domain_scores_gemma":[0.994598,0.0007266852,0.0004122194,0.0004268757,0.002360774,0.001475335],"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":[0.0003984254,0.00009234476,0.001624761,0.0008712525,0.0000399641,0.000200647,0.0000954122,0.000491656,0.0035637,0.00408488,0.8402027,0.1483341],"study_design_scores_gemma":[0.0001408349,0.0001510677,0.001991014,0.0003732129,0.00002769723,0.0001621286,0.00009365482,0.001232658,0.002194785,0.002430862,0.9911715,0.00003041647],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"commentary","genre_scores_codex":[0.01804683,0.02244729,0.04250953,0.1075485,0.04816819,0.0032698,0.1101385,0.04807478,0.5997966],"genre_scores_gemma":[0.1314705,0.03501231,0.1096769,0.06311576,0.02286575,0.002922178,0.1670653,0.00599635,0.4618751],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.2706158,"threshold_uncertainty_score":0.9053002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009616489974619495,"score_gpt":0.2678902258289754,"score_spread":0.2582737358543559,"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."}}