{"id":"W2960603005","doi":"10.1128/jcm.00300-19","title":"Molecular Diagnosis of Vaginitis: Comparing Quantitative PCR and Microbiome Profiling Approaches to Current Microscopy Scoring","year":2019,"lang":"en","type":"article","venue":"Journal of Clinical Microbiology","topic":"Reproductive tract infections research","field":"Immunology and Microbiology","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Health Services; Calgary Laboratory Services; University of Calgary","funders":"Natural Environment Research Council; Sight Research UK; Calgary Laboratory Services","keywords":"Vaginitis; Microbiome; Profiling (computer programming); Biology; Computational biology; Medicine; Microbiology; Bioinformatics; Computer science; Genetics","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":[0.00797432,0.0008185562,0.001000348,0.002770478,0.0003646658,0.002932298,0.0009621888,0.001367353,0.001305131],"category_scores_gemma":[0.01020067,0.000532847,0.0006666118,0.001452933,0.0008616261,0.001375071,0.0009153206,0.001160852,0.0008533269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000749375,"about_ca_system_score_gemma":0.0004409666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001056666,"about_ca_topic_score_gemma":0.002006907,"domain_scores_codex":[0.991769,0.003436102,0.0003763248,0.001546404,0.002447007,0.0004252204],"domain_scores_gemma":[0.9939938,0.002770045,0.001136508,0.0003450906,0.001471559,0.0002830191],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01178866,0.002074569,0.4915635,0.002437136,0.001925304,0.0002613096,0.001042555,0.003190018,0.157274,0.002105969,0.003276608,0.3230604],"study_design_scores_gemma":[0.0003993676,0.01627277,0.7889932,0.001127327,0.00152049,0.004096977,0.00385855,0.0595526,0.1047564,0.003832596,0.01523305,0.0003568227],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.889819,0.02269872,0.07092752,0.002110478,0.001293073,0.0006950159,0.002156984,0.0005727122,0.009726562],"genre_scores_gemma":[0.9038897,0.006904637,0.08191792,0.001655632,0.0003934984,0.0003633009,0.001938515,0.0001231008,0.002813769],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00797432,"threshold_uncertainty_score":0.04217279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2461273088023982,"score_gpt":0.4325761319946267,"score_spread":0.1864488231922285,"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."}}