{"id":"W3175812194","doi":"10.1096/fasebj.2019.33.1_supplement.758.5","title":"Determining Risk Factors for Triple Whammy AKI using Computational Models of Long‐ Term Blood Pressure Regulation","year":2019,"lang":"en","type":"article","venue":"The FASEB Journal","topic":"Renal function and acid-base balance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Institute of Diabetes and Digestive and Kidney Diseases","keywords":"Medicine; Nephron; Blood pressure; Angiotensin II; Acute kidney injury; Renal function; Internal medicine; Tubuloglomerular feedback; Angiotensin receptor; Renin–angiotensin system; Kidney; Intensive care medicine; Pharmacology; Endocrinology; Cardiology; Bioinformatics; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0006385752,0.0006669966,0.000940343,0.0005567325,0.0005972821,0.001285569,0.00127382,0.00159628,0.002628957],"category_scores_gemma":[0.00463167,0.0005854219,0.001198287,0.0005007779,0.0005829986,0.0006954432,0.001002134,0.001116829,0.0001949936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001031162,"about_ca_system_score_gemma":0.001907247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03992001,"about_ca_topic_score_gemma":0.0263709,"domain_scores_codex":[0.9997759,0.00008212527,0.0000117457,0.0000485932,0.00002296392,0.00005858229],"domain_scores_gemma":[0.9966102,0.002508865,0.0002838766,0.00007932365,0.0002461823,0.0002715313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007678198,0.0000769406,0.008056504,0.00001902215,0.00006057087,0.0001028917,0.00003906887,0.9879814,0.0001022232,0.001554321,0.0006400455,0.001290291],"study_design_scores_gemma":[0.00001397616,0.00001104401,0.000647075,0.00000369972,0.000009965543,0.000007520062,0.0000204831,0.9982785,0.00002184635,0.0008687965,0.0001112745,0.000005805281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9301626,0.0004914232,0.05700541,0.002766847,0.0002100424,0.00008865275,0.001909322,0.0003008873,0.007064809],"genre_scores_gemma":[0.9882014,0.0002259467,0.008069142,0.000269866,0.00005076724,0.0001613903,0.001078017,0.00004756718,0.001895839],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03992001,"threshold_uncertainty_score":0.07937527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0341289518070129,"score_gpt":0.2788543487608687,"score_spread":0.2447253969538558,"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."}}