{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00397982,0.0008923078,0.001634566,0.004499373,0.0005641394,0.002739312,0.001374045,0.00234671,0.005023738],"category_scores_gemma":[0.0106063,0.0003598253,0.001669431,0.006128201,0.00147397,0.00318309,0.002080466,0.004100956,0.00126831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001849715,"about_ca_system_score_gemma":0.006225226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002451811,"about_ca_topic_score_gemma":0.002701001,"domain_scores_codex":[0.9971572,0.001041806,0.0005280351,0.0002649456,0.000877833,0.0001301429],"domain_scores_gemma":[0.989606,0.007469481,0.001013826,0.0003423362,0.001253362,0.0003149865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008899214,0.00005211838,0.001236155,0.06829546,0.0007934275,0.0003597449,0.000337591,0.0006491927,0.0002946423,0.02735825,0.08812857,0.8124059],"study_design_scores_gemma":[0.00001963397,0.00004653763,0.002271315,0.03308624,0.0003274486,0.001347614,0.0001944909,0.0001810634,0.0001779697,0.01736983,0.9449362,0.00004164824],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00008261145,0.9948133,0.000261567,0.003311643,0.0005741846,0.00001352276,0.00005561737,0.00001256866,0.0008749298],"genre_scores_gemma":[0.001214322,0.9956335,0.0003733695,0.001576698,0.0008674012,0.00002511842,0.00008786584,0.000004079788,0.0002174584],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.005023738,"threshold_uncertainty_score":0.02104753,"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."}}