{"id":"W4280523107","doi":"10.1088/1361-6579/ac8ccd","title":"Machine learning to support triage of children at risk for epileptic seizures in the pediatric intensive care unit","year":2022,"lang":"en","type":"article","venue":"Physiological Measurement","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Children's Hospital; St. Michael's Hospital; University of Calgary; University of Toronto","funders":"","keywords":"Triage; Medicine; Receiver operating characteristic; Intensive care unit; Pediatric intensive care unit; Early warning score; Intensive care medicine; Intensive care; Emergency medicine; Emergency department; Medical emergency; Pediatrics; Internal medicine; Psychiatry","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":[0.0007609994,0.0001510546,0.0002579644,0.00008385071,0.0003074862,0.00001424124,0.0005603913,0.00002493224,0.000101302],"category_scores_gemma":[0.001536067,0.00009272043,0.0001245988,0.0003128097,0.00003918743,0.00002379437,0.0003925473,0.0003500105,0.00000927738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009299162,"about_ca_system_score_gemma":0.00002375034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003891108,"about_ca_topic_score_gemma":0.0000107396,"domain_scores_codex":[0.9976004,0.0008669189,0.0002942571,0.0004119819,0.00054608,0.0002803904],"domain_scores_gemma":[0.9990233,0.0004156398,0.0001642711,0.0002033702,0.0001471674,0.00004628614],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.002874994,0.001170921,0.2634273,0.0002045678,0.00006996092,0.00004834922,0.02871828,0.09364898,0.5907935,0.0003516073,0.0062285,0.01246306],"study_design_scores_gemma":[0.007216918,0.02881409,0.793395,0.00005134916,0.0002469294,0.00009827624,0.01252792,0.003266928,0.1459952,0.001822759,0.005243858,0.001320747],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982684,0.0001675508,0.00006362097,0.0001985228,0.0001309931,0.0009591436,0.0001075581,0.00002863787,0.00007558858],"genre_scores_gemma":[0.9984134,0.0000216207,0.00004137937,0.001172546,0.00008127971,0.0002260252,0.00001472497,0.000009080441,0.00001992615],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5299677,"threshold_uncertainty_score":0.378103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1158566415492074,"score_gpt":0.3037230320524615,"score_spread":0.1878663905032541,"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."}}