{"id":"W2913725813","doi":"10.1016/j.ebiom.2019.01.023","title":"Integrating exosomal microRNAs and electronic health data improved tuberculosis diagnosis","year":2019,"lang":"en","type":"article","venue":"EBioMedicine","topic":"Extracellular vesicles in disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Tuberculosis; microRNA; Microvesicles; Medicine; Bioinformatics; Virology; Computational biology; Immunology; Biology; Pathology; Genetics; Gene","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003933137,0.0007413236,0.0008601541,0.001284739,0.0002369276,0.001708918,0.0004211658,0.0008052016,0.001456487],"category_scores_gemma":[0.0117451,0.0002596992,0.0008517226,0.0009721836,0.0002117997,0.00126638,0.001048825,0.000829375,0.0005646701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004436465,"about_ca_system_score_gemma":0.0005852331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001299895,"about_ca_topic_score_gemma":0.001561461,"domain_scores_codex":[0.9972208,0.001634515,0.0002168893,0.0005393911,0.0002394027,0.0001491267],"domain_scores_gemma":[0.9945945,0.003573684,0.0007669129,0.0002686724,0.0006150507,0.0001812517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002272191,0.0005751621,0.6619965,0.0005678597,0.0007784444,0.0002878111,0.0002102777,0.02211728,0.005916388,0.0008451102,0.003316849,0.3011161],"study_design_scores_gemma":[0.0001404296,0.001545301,0.2475411,0.0004350423,0.001364072,0.001130851,0.000393243,0.7208226,0.01328174,0.005269203,0.007968371,0.0001081083],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9038957,0.009172477,0.07421298,0.003176973,0.0003203143,0.0002064618,0.004271551,0.0009510613,0.003792349],"genre_scores_gemma":[0.9729599,0.000872462,0.0239364,0.0002659392,0.0001393331,0.00004642331,0.001314286,0.00001908169,0.0004461836],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003933137,"threshold_uncertainty_score":0.02080065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008120717840185,"score_gpt":0.2719489267923659,"score_spread":0.2638282089521809,"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."}}