{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009231099,0.001126306,0.0007713984,0.002562386,0.0002770231,0.001185537,0.0004020174,0.000817911,0.001792579],"category_scores_gemma":[0.001219027,0.0002783641,0.0006604865,0.001297652,0.0001855365,0.0003888332,0.0006734032,0.000490732,0.001099927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006622823,"about_ca_system_score_gemma":0.0005520917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001190087,"about_ca_topic_score_gemma":0.001493671,"domain_scores_codex":[0.9994641,0.0001475757,0.00003252176,0.0001723253,0.0001335912,0.00004990421],"domain_scores_gemma":[0.9996105,0.00008842671,0.00009002951,0.00002926105,0.0001319138,0.00004988867],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001944597,0.0004410735,0.140517,0.000858227,0.0007547868,0.0005791303,0.0001211655,0.005642602,0.6597986,0.001786195,0.007411411,0.1801454],"study_design_scores_gemma":[0.0001378874,0.001762441,0.2515847,0.0002073988,0.0007634874,0.002026181,0.0003897333,0.1174457,0.5844025,0.008377791,0.03270974,0.0001924834],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6843134,0.03032871,0.2377944,0.004687807,0.000898403,0.001245991,0.02022798,0.004990533,0.01551282],"genre_scores_gemma":[0.8532598,0.005506028,0.1286095,0.001080473,0.0003030826,0.0003948947,0.006181724,0.0001224991,0.004541947],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002562386,"threshold_uncertainty_score":0.005996764,"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."}}