{"id":"W2790031523","doi":"10.1159/000484963","title":"Acute Kidney Injury and Big Data","year":2018,"lang":"en","type":"review","venue":"Contributions to nephrology","topic":"Acute Kidney Injury Research","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Milestone; Medicine; Big data; Acute kidney injury; Informatics; Health care; Health informatics; Intensive care medicine; Exploit; Benchmarking; Health information technology; Quality (philosophy); Acute care; Medical emergency; Data science; Public health; Data mining; Nursing; Internal medicine; Computer security; Computer science; Business; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008869878,0.0005559779,0.002565148,0.0007810761,0.0002712548,0.00005617541,0.001054166,0.001043511,0.0005067959],"category_scores_gemma":[0.0045217,0.0004485115,0.0002242226,0.000958245,0.0006784523,0.00009649569,0.00253474,0.001101414,0.002134145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003118308,"about_ca_system_score_gemma":0.003815244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001667377,"about_ca_topic_score_gemma":0.00000346648,"domain_scores_codex":[0.9962322,0.0003880228,0.0007939211,0.001270287,0.0003120321,0.001003611],"domain_scores_gemma":[0.9940863,0.0002663307,0.0002056084,0.003147465,0.000589109,0.00170519],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001108679,0.00005258803,0.000002846381,0.0008825604,0.000947087,0.00008852289,0.000005559871,8.023705e-11,0.00001083083,0.0002093098,0.6873343,0.3103555],"study_design_scores_gemma":[0.0008204922,0.000915853,0.00000806498,0.001484567,0.00460761,0.001427575,0.000001060232,0.000005396165,0.00000307038,0.0000626621,0.9903299,0.0003337517],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000002776344,0.8679552,0.000803672,0.01196161,0.000791373,0.002692134,0.114865,0.0001416373,0.0007866493],"genre_scores_gemma":[0.000003077701,0.9603571,0.0004549575,0.008717936,0.00286663,0.0004763595,0.0245107,0.00008195023,0.002531316],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.3100218,"threshold_uncertainty_score":0.9997967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1157149334776021,"score_gpt":0.4559195173324609,"score_spread":0.3402045838548589,"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."}}