{"id":"W4225979822","doi":"10.1017/s1047951122000932","title":"Machine learning predicts blood lactate levels in children after cardiac surgery in paediatric ICU","year":2022,"lang":"en","type":"article","venue":"Cardiology in the Young","topic":"Hemodynamic Monitoring and Therapy","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"Murata Science Foundation; Tateishi Science and Technology Foundation; Research Promotion Foundation; St. Francis Xavier University","keywords":"Perioperative; Cardiac surgery; Blood pressure; Blood lactate; Hemodynamics; Random forest; Mean arterial pressure","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.002837635,0.0001726064,0.0006400654,0.0004177151,0.00007118429,0.000007215081,0.0001538911,0.0001124887,0.00002264671],"category_scores_gemma":[0.000174431,0.0001405808,0.0001836665,0.0005816352,0.00005303425,0.00003439542,0.00009598043,0.001250051,0.000005832727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001469465,"about_ca_system_score_gemma":0.00009725329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003275753,"about_ca_topic_score_gemma":0.0000107651,"domain_scores_codex":[0.9971196,0.001526584,0.0003521932,0.000322072,0.000228887,0.0004506543],"domain_scores_gemma":[0.9991317,0.0004321569,0.00006260266,0.0003136336,0.00001390078,0.00004604065],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001166849,0.0000485726,0.9942001,0.00001047952,0.000112053,0.00208642,0.001168663,0.001269016,0.0000900219,0.000005596553,0.00004858607,0.0008438089],"study_design_scores_gemma":[0.001000943,0.00009023425,0.9962847,0.00001951326,0.00006349786,0.001667033,0.0002336192,0.0002113577,0.00001153446,0.00006331836,0.0002208805,0.0001333334],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9921045,0.005837604,0.000004024945,0.0002667402,0.0006691666,0.0004029756,0.00005500484,0.00003868865,0.0006213122],"genre_scores_gemma":[0.9980683,0.0007456833,0.000009300791,0.0001419787,0.0004919464,0.0003107371,0.00009187871,0.00002820279,0.000112018],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005963771,"threshold_uncertainty_score":0.5732717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01071250333295134,"score_gpt":0.2325236074818205,"score_spread":0.2218111041488692,"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."}}