{"id":"W3159163656","doi":"10.1101/2021.04.07.21255028","title":"Metabolomics strategy for diagnosing urinary tract infections","year":2021,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Urinary Tract Infections Management","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary; Alberta Innovates; International Microbiome Centre, University of Calgary; Alberta Precision Laboratories; Genome Canada","keywords":"Urinary system; Metabolomics; Medicine; Intensive care medicine; Computer science; Computational biology; Biology; Internal medicine; Bioinformatics","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004747635,0.0004349368,0.0007352249,0.0004342578,0.00021338,0.0001544002,0.0001694111,0.000373437,0.000316608],"category_scores_gemma":[0.0003635845,0.0004551055,0.0006220461,0.0002928153,0.00007093163,0.0001526759,0.000370001,0.001047401,0.00003008185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002539418,"about_ca_system_score_gemma":0.0003990509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007781982,"about_ca_topic_score_gemma":0.00001716509,"domain_scores_codex":[0.9978627,0.000102008,0.0005738032,0.0007710966,0.0002664136,0.000424003],"domain_scores_gemma":[0.9979431,0.0003218671,0.000254848,0.001016469,0.0002725268,0.0001911603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000423226,0.1622423,0.7473908,0.008453532,0.006561487,0.004001088,0.001079705,0.03209027,0.001901152,0.003999411,0.006756773,0.0251002],"study_design_scores_gemma":[0.003688709,0.0101014,0.7938222,0.001464632,0.006503867,0.002303297,0.0006514348,0.004020488,0.003418443,0.001595407,0.1706922,0.001737909],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9750479,0.001954085,0.01058092,0.001221931,0.002252792,0.001964916,0.00005250385,0.0003630621,0.006561892],"genre_scores_gemma":[0.9904897,0.001371258,0.003874461,0.0002560338,0.000766967,0.001200124,0.0004711752,0.0001081928,0.001462042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1639354,"threshold_uncertainty_score":0.9997901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0600743302921969,"score_gpt":0.3362872783350204,"score_spread":0.2762129480428235,"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."}}