{"id":"W2994263134","doi":"10.1109/embc44109.2020.9176452","title":"Individualized closed-loop anesthesia through patient model partitioning","year":2020,"lang":"en","type":"article","venue":"","topic":"Anesthesia and Sedative Agents","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Dosing; SAFER; Controller (irrigation); Closed loop; Computer science; A priori and a posteriori; Sensitivity (control systems); Set (abstract data type); Pharmacodynamics; Control theory (sociology); Medicine; Artificial intelligence; Control engineering; Pharmacokinetics; Control (management); Pharmacology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004472799,0.0001413754,0.0002601912,0.00001943845,0.00008081002,0.0000180364,0.00006425776,0.00006686852,0.0006763185],"category_scores_gemma":[0.00004286968,0.000110288,0.00009145937,0.0001536611,0.00004543633,0.0001381077,0.00002164745,0.0001188199,0.0003114662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001887014,"about_ca_system_score_gemma":0.00006390931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001402616,"about_ca_topic_score_gemma":4.512034e-7,"domain_scores_codex":[0.9989556,0.00003985556,0.0002385629,0.0002537516,0.0002999953,0.0002122261],"domain_scores_gemma":[0.9995234,0.0000225294,0.00006101951,0.0001530866,0.00006241709,0.0001775189],"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":[0.004204532,0.00330014,0.3109422,0.0008286111,0.001288831,0.006164243,0.128212,0.005450266,0.02890858,0.136136,0.3298077,0.04475688],"study_design_scores_gemma":[0.02356951,0.007237027,0.0598991,0.0005239933,0.001503237,0.001272865,0.0119818,0.3818595,0.1228318,0.008848429,0.3776399,0.002832786],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9517045,0.00006550436,0.01153372,0.01571872,0.000009230629,0.0003716788,0.000001728712,0.0001806642,0.02041423],"genre_scores_gemma":[0.9470965,0.0000184918,0.01201813,0.03990377,0.0000536446,0.00002365367,0.00005965052,0.00002298014,0.0008031638],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3764093,"threshold_uncertainty_score":0.7405213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07248872533962418,"score_gpt":0.2951850020614912,"score_spread":0.222696276721867,"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."}}