{"id":"W3041446232","doi":"10.1101/2020.07.07.192138","title":"Recurrent Neural Network-based Acute Concussion Classifier using Raw Resting State EEG Data","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Traumatic Brain Injury Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Western University; University of Victoria","funders":"Western Canada Research Grid; Compute Canada","keywords":"Concussion; Electroencephalography; Medicine; Athletes; Physical medicine and rehabilitation; Physical therapy; Psychology; Injury prevention; Poison control; Psychiatry; Medical emergency","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":[0.0005285917,0.0007440896,0.0004613283,0.0006175805,0.0002420096,0.0004231294,0.0006002586,0.0005480457,0.001118154],"category_scores_gemma":[0.001403297,0.0001719535,0.0004038303,0.0002910522,0.0001444438,0.0003372523,0.0004020624,0.0004945525,0.0003447756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004305587,"about_ca_system_score_gemma":0.0005055332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01037445,"about_ca_topic_score_gemma":0.009863585,"domain_scores_codex":[0.9998005,0.00002989432,0.00001926602,0.00005844483,0.00004034095,0.00005160538],"domain_scores_gemma":[0.9996613,0.0001041051,0.00004446214,0.00002465205,0.0001410322,0.00002452706],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001843067,0.001023784,0.05954364,0.0002940327,0.000463226,0.001162273,0.000227177,0.2546228,0.05638406,0.0004659722,0.005506538,0.6184635],"study_design_scores_gemma":[0.00001485233,0.0003163302,0.01325647,0.00001645306,0.00005216426,0.0001076236,0.0000482131,0.9780549,0.007741796,0.0001389991,0.000238634,0.00001346453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9336029,0.0006496495,0.06196827,0.0001700622,0.0001070938,0.0000997618,0.0006274285,0.001209256,0.001565549],"genre_scores_gemma":[0.9890632,0.0001142835,0.00912746,0.00002431049,0.00001798524,0.00003805084,0.0006370141,0.00001151199,0.0009660755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01037445,"threshold_uncertainty_score":0.02062815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1461957482050896,"score_gpt":0.3459540126234062,"score_spread":0.1997582644183167,"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."}}