{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009490044,0.0001898279,0.0005348554,0.0001222013,0.0003630476,0.000250136,0.0008094685,0.0001138763,0.000003880708],"category_scores_gemma":[0.0008518199,0.0001413034,0.00009824004,0.0005187031,0.0002543165,0.0002556111,0.000325824,0.0002749168,6.33852e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007964051,"about_ca_system_score_gemma":0.0001330108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002860546,"about_ca_topic_score_gemma":0.00001135127,"domain_scores_codex":[0.9975852,0.0003145778,0.0004738353,0.0009229368,0.0003438059,0.0003596104],"domain_scores_gemma":[0.9986579,0.0002783685,0.0001361552,0.0006604755,0.00007440257,0.0001926825],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004900245,0.006511039,0.1012052,0.01215384,0.0002074437,0.001946531,0.01493886,0.01729226,0.7250267,0.02730571,0.02595766,0.06255452],"study_design_scores_gemma":[0.008865366,0.002726545,0.05216587,0.003985834,0.0002641587,0.0003107076,0.007924451,0.8871716,0.009350442,0.001779918,0.02415601,0.001299114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.088443,0.0004325996,0.9070043,0.0004487333,0.001810495,0.00172718,0.00001161552,0.00004517917,0.00007692544],"genre_scores_gemma":[0.8956739,0.000008932453,0.09341714,0.008926053,0.0002837384,0.0004132252,0.00004199895,0.00004960649,0.001185467],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8698793,"threshold_uncertainty_score":0.5762184,"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."}}