{"id":"W6902045691","doi":"10.6084/m9.figshare.19793310","title":"Additional file 4 of Prediction of acute kidney injury risk after cardiac surgery: using a hybrid machine learning algorithm","year":2022,"lang":"en","type":"article","venue":"Figshare","topic":"Acute Kidney Injury Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Univariate; Table (database); Acute kidney injury; Multivariate statistics; Univariate analysis; Multivariate analysis","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001612735,0.001139841,0.001470218,0.002001982,0.0006488697,0.001558796,0.001759081,0.001175899,0.8421628],"category_scores_gemma":[0.03590211,0.0004685433,0.00129134,0.003320376,0.0002231985,0.001516458,0.0008370505,0.001136947,0.1207917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008008254,"about_ca_system_score_gemma":0.001298119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006501925,"about_ca_topic_score_gemma":0.01050929,"domain_scores_codex":[0.9990833,0.0002121851,0.0001744026,0.0002156982,0.0001850464,0.0001294169],"domain_scores_gemma":[0.971832,0.02331457,0.001370115,0.0009269552,0.002123967,0.0004323425],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008266203,0.0001266909,0.007202162,0.003238064,0.0001595089,0.0000879439,0.0000418845,0.0007958724,0.00008023206,0.0005631317,0.9760477,0.01083011],"study_design_scores_gemma":[0.01572336,0.0008506625,0.09539875,0.007523569,0.001028781,0.001116534,0.0006650012,0.007333324,0.001361604,0.0205799,0.8481014,0.0003171322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.000312359,0.00003849249,0.0001857658,0.0001088125,0.00002019805,0.00005021373,0.9984776,0.0001602489,0.0006462293],"genre_scores_gemma":[0.0172906,0.000267491,0.003152261,0.0007655956,0.0001907034,0.001739853,0.9640743,0.0007498825,0.01176934],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8421628,"threshold_uncertainty_score":0.2251354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02377139505749802,"score_gpt":0.2716413443820929,"score_spread":0.2478699493245948,"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."}}