{"id":"W2112148660","doi":"10.1109/iembs.2004.1403276","title":"Quantifying uncertainty bounds in anesthetic PKPD models","year":2005,"lang":"en","type":"article","venue":"","topic":"Anesthesia and Sedative Agents","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Stability (learning theory); Population; Computer science; Anesthetic; Mathematical optimization; Mathematics; Control (management); Control theory (sociology); Machine learning; Anesthesia; Artificial intelligence; Medicine","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":[0.0001524473,0.0001014031,0.0001975834,0.00009594281,0.00003433873,0.00001160196,0.00005740943,0.00005800718,0.0003938335],"category_scores_gemma":[0.000009792391,0.0000770425,0.00005138205,0.0001510825,0.00004076847,0.000124326,0.00001165305,0.0001104841,0.0001833612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000745738,"about_ca_system_score_gemma":0.00005062722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001685024,"about_ca_topic_score_gemma":0.0001758647,"domain_scores_codex":[0.9992183,0.00002813389,0.0001868917,0.0001819528,0.0001672247,0.0002174437],"domain_scores_gemma":[0.9996566,0.00002562706,0.00002397433,0.0001788886,0.0000322427,0.00008266862],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001184676,0.002666337,0.7028853,0.0002780741,0.0001362581,0.001710347,0.01023059,0.02879811,0.006387813,0.132607,0.007634188,0.1054814],"study_design_scores_gemma":[0.007580637,0.0006422815,0.1926523,0.0003486659,0.0001129033,0.0009323979,0.003402156,0.7019676,0.002535017,0.002647217,0.08636352,0.0008153148],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9728716,0.0001219163,0.0009443971,0.002899026,0.0000135956,0.0001647268,2.094826e-7,0.00005129528,0.02293321],"genre_scores_gemma":[0.9915787,0.00002763275,0.00140623,0.002426078,0.00004838163,0.000008322875,0.000009482549,0.00001290528,0.004482283],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6731695,"threshold_uncertainty_score":0.43122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08570525971145196,"score_gpt":0.3283898862267663,"score_spread":0.2426846265153144,"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."}}