{"id":"W6958300502","doi":"10.6084/m9.figshare.19793295.v1","title":"Additional file 10 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":"Logistic regression; Acute kidney injury; Receiver operating characteristic; Calibration; Risk assessment; Regression","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":[0.001849714,0.001065727,0.001052313,0.001850616,0.0005160202,0.001352638,0.001590314,0.001216146,0.797725],"category_scores_gemma":[0.02875688,0.0004265076,0.001067635,0.002468666,0.0002026791,0.001134879,0.0008357522,0.0009534831,0.1485615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007552588,"about_ca_system_score_gemma":0.001195303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005665681,"about_ca_topic_score_gemma":0.01141106,"domain_scores_codex":[0.9992459,0.0001634998,0.000125608,0.0002103042,0.0001575046,0.00009734079],"domain_scores_gemma":[0.9778741,0.01778044,0.0009404651,0.0009876676,0.002068094,0.0003491388],"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":[0.0004433092,0.0001167936,0.004694503,0.001489822,0.00007541109,0.00005929435,0.00002562559,0.00072273,0.00007748871,0.0002835046,0.9834093,0.008602169],"study_design_scores_gemma":[0.0113877,0.0007282827,0.08362417,0.004026479,0.0005135179,0.0007758517,0.0005116762,0.008586012,0.00169442,0.0119409,0.8759542,0.0002569267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.000264436,0.00001469428,0.0001571516,0.00007801715,0.00001517208,0.00003012917,0.9988669,0.0002105545,0.0003629521],"genre_scores_gemma":[0.008450007,0.00007794332,0.002113322,0.0003211948,0.00009141918,0.0008208063,0.9825543,0.0004444839,0.005126582],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.797725,"threshold_uncertainty_score":0.2885207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02492998278431283,"score_gpt":0.2760907832999329,"score_spread":0.2511608005156201,"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."}}