{"id":"W3172536974","doi":"10.3389/fnhum.2021.675154","title":"Building an Open Source Classifier for the Neonatal EEG Background: A Systematic Feature-Based Approach From Expert Scoring to Clinical Visualization","year":2021,"lang":"en","type":"article","venue":"Frontiers in Human Neuroscience","topic":"Neonatal and fetal brain pathology","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto; Université de Montréal; SickKids Foundation; Centre Hospitalier Universitaire Sainte-Justine","funders":"H2020 Marie Skłodowska-Curie Actions; European Commission; Eunice Kennedy Shriver National Institute of Child Health and Human Development; Medical Research Council; Suomen Aivosäätiö; Canadian Institutes of Health Research; Sigrid Juséliuksen Säätiö; National Institutes of Health; National Health and Medical Research Council; Suomalainen Tiedeakatemia; Lastentautien Tutkimussäätiö; Hospital for Sick Children","keywords":"Classifier (UML); Computer science; Electroencephalography; Visualization; Artificial intelligence; Pattern recognition (psychology); Machine learning; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002918079,0.001394459,0.001201861,0.00225749,0.0004290641,0.001933785,0.001658682,0.001123828,0.003400493],"category_scores_gemma":[0.01278457,0.0003937703,0.0009083062,0.000867874,0.0002916966,0.001414666,0.001486977,0.001582016,0.002967138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005814797,"about_ca_system_score_gemma":0.001316085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00191923,"about_ca_topic_score_gemma":0.00210588,"domain_scores_codex":[0.9978852,0.0003693321,0.0002112132,0.0006524143,0.0007055051,0.0001763223],"domain_scores_gemma":[0.9951579,0.001737373,0.0002558104,0.0006241906,0.002009868,0.0002149231],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000488452,0.0003664089,0.007845095,0.0002683691,0.0001531962,0.000366241,0.0001756579,0.01644391,0.03003401,0.00151053,0.01791795,0.9244302],"study_design_scores_gemma":[0.0001229198,0.0004134789,0.009291735,0.0001525516,0.0001072837,0.0006438502,0.0001458192,0.9050598,0.06050031,0.005783751,0.0176833,0.0000952768],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02463362,0.0002366715,0.9433585,0.0002025243,0.0001233282,0.0004782946,0.001019972,0.02897893,0.0009682336],"genre_scores_gemma":[0.1937746,0.0002225776,0.7956887,0.0001708668,0.00009318259,0.001022873,0.004522118,0.001952811,0.002552279],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003400493,"threshold_uncertainty_score":0.01543248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1034525182248275,"score_gpt":0.3931838085504367,"score_spread":0.2897312903256092,"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."}}