{"id":"W6939206631","doi":"10.6084/m9.figshare.19793301.v1","title":"Additional file 1 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":"Acute kidney injury; Table (database); Risk assessment; Kidney disease; Key (lock)","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":[0.001549708,0.0009963048,0.001192751,0.001876955,0.0005990421,0.001426752,0.00148403,0.001092663,0.8645192],"category_scores_gemma":[0.03748839,0.0004628287,0.0009345819,0.003121036,0.0002187792,0.001450005,0.0007722619,0.001085178,0.1491518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007605738,"about_ca_system_score_gemma":0.001318673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00500815,"about_ca_topic_score_gemma":0.008093328,"domain_scores_codex":[0.9992322,0.0001826086,0.0001427351,0.0001991053,0.0001484533,0.00009501624],"domain_scores_gemma":[0.9718655,0.02366855,0.001141026,0.0009366878,0.001968713,0.0004194762],"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":[0.0006074854,0.0001113264,0.003915951,0.002417315,0.00008197851,0.00006331166,0.00003374552,0.0006161836,0.00005765841,0.0005644637,0.9799089,0.01162163],"study_design_scores_gemma":[0.01330288,0.0008977738,0.06650472,0.006696689,0.0006148026,0.00114824,0.0005599401,0.006591764,0.001198222,0.02214896,0.8800573,0.0002787551],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.000282752,0.00003371794,0.000235409,0.0001254878,0.00002193476,0.00006251111,0.9982983,0.0001505855,0.0007893028],"genre_scores_gemma":[0.01629211,0.0002929002,0.003849081,0.0008713914,0.0002173351,0.002168441,0.9635271,0.0007560978,0.0120255],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8645192,"threshold_uncertainty_score":0.1932468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02401388333490083,"score_gpt":0.2716547926566189,"score_spread":0.2476409093217181,"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."}}