{"id":"W2741843236","doi":"10.1038/s41598-017-07613-x","title":"Standardization of electroencephalography for multi-site, multi-platform and multi-investigator studies: insights from the canadian biomarker integration network in depression","year":2017,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital; Baycrest Hospital; McMaster University; Indoc Research; Canada Research Chairs; Queen's University; St. Joseph’s Healthcare Hamilton; University of British Columbia; Simon Fraser University; University Health Network; University of Toronto; Vancouver Coastal Health; Centre for Addiction and Mental Health","funders":"Canadian Institutes of Health Research; H. Lundbeck A/S; Government of Ontario; Natural Sciences and Engineering Research Council of Canada; Pfizer; Servier; Ontario Brain Institute","keywords":"Standardization; Computer science; Data science; Documentation; Data integration; Toolbox; Neuroinformatics; Data collection; Preprocessor; Electroencephalography; Data mining; Artificial intelligence; Neuroscience; Psychology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.1393503,0.0008875842,0.0009362502,0.004873421,0.004758539,0.006460875,0.005850981,0.001448395,0.001125958],"category_scores_gemma":[0.1927588,0.0007230234,0.000744765,0.009458302,0.005148557,0.002960611,0.007827149,0.002354842,0.0004948348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0354598,"about_ca_system_score_gemma":0.1586817,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.7966424,"about_ca_topic_score_gemma":0.8918135,"domain_scores_codex":[0.9045745,0.04517588,0.00882638,0.006109524,0.03132077,0.003992993],"domain_scores_gemma":[0.7295873,0.04264238,0.01474156,0.03435458,0.16936,0.00931418],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009026095,0.0001803689,0.1049738,0.001926121,0.0003963788,0.0005099778,0.02129662,0.007171512,0.008785731,0.05663443,0.07841308,0.7188094],"study_design_scores_gemma":[0.0004164171,0.0004544266,0.4952125,0.004536946,0.000648007,0.0008521848,0.01010866,0.01869249,0.01385598,0.05699647,0.3974599,0.0007660157],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1587574,0.02433419,0.5673453,0.1499267,0.002552688,0.009355132,0.01562441,0.002968659,0.06913544],"genre_scores_gemma":[0.4085629,0.008626691,0.5571076,0.005918307,0.0006123798,0.004181664,0.00667834,0.0009414216,0.007370691],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7966424,"threshold_uncertainty_score":0.7369639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1122823504138155,"score_gpt":0.33046680072857,"score_spread":0.2181844503147545,"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."}}