{"id":"W3139561902","doi":"10.2139/ssrn.3247844","title":"Integrating Exosomal MicroRNA and Electronic Health Data Improves Tuberculosis Diagnosis","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Extracellular vesicles in disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"microRNA; Tuberculosis; Medicine; Data science; Computer science; Computational biology; Bioinformatics; Pathology; Biology; Genetics","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":[0.002376881,0.000450526,0.0006467446,0.001492598,0.0002426343,0.002511603,0.0004801636,0.001166509,0.005904474],"category_scores_gemma":[0.01272832,0.000237723,0.0004203674,0.001267734,0.0001812787,0.001795723,0.001323934,0.0006250222,0.003650254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003486681,"about_ca_system_score_gemma":0.0004314256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005714464,"about_ca_topic_score_gemma":0.0009457826,"domain_scores_codex":[0.9977388,0.0009479045,0.0002816524,0.0003832222,0.000532933,0.0001154384],"domain_scores_gemma":[0.9930336,0.003655138,0.0008076485,0.0007713471,0.001549874,0.0001823306],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00342102,0.0007639988,0.2866594,0.001376159,0.0005635002,0.0007521806,0.0003029093,0.006007952,0.05890408,0.003028494,0.02312294,0.6150973],"study_design_scores_gemma":[0.0003486215,0.002242176,0.2608172,0.001281702,0.001631163,0.003765111,0.001329893,0.2817942,0.3088914,0.02447508,0.1130977,0.0003259185],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7825485,0.01383169,0.1209584,0.0104405,0.001844635,0.0004393629,0.03585033,0.006468813,0.02761776],"genre_scores_gemma":[0.9201287,0.002004274,0.06075963,0.001343424,0.0005164184,0.0001203767,0.01113089,0.0001773496,0.003818875],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005904474,"threshold_uncertainty_score":0.01975238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009065897546848656,"score_gpt":0.2736265641003362,"score_spread":0.2645606665534875,"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."}}