{"id":"W2567134835","doi":"10.14264/uql.2016.613","title":"Investigating the ability of post-stroke EEG measures of brain dysfuntion to inform early prediction of cognitive impairment or depression outcomes","year":2016,"lang":"en","type":"dissertation","venue":"The University of Queensland","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quantitative electroencephalography; Montreal Cognitive Assessment; Context (archaeology); Cognition; Stroke (engine); Psychology; Functional Independence Measure; Electroencephalography; Dementia; Clinical psychology; Physical medicine and rehabilitation; Audiology; Medicine; Activities of daily living; Cognitive impairment; Psychiatry; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001970201,0.0004603417,0.0002132567,0.0006034712,0.0001399407,0.0007531332,0.0002939224,0.0005026495,0.001907447],"category_scores_gemma":[0.005438726,0.0001319924,0.0002736376,0.0004314394,0.0002018894,0.0003901132,0.0002996321,0.0003672799,0.0004970557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001450577,"about_ca_system_score_gemma":0.0003115911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007122512,"about_ca_topic_score_gemma":0.001250455,"domain_scores_codex":[0.9995852,0.0001897413,0.00003184317,0.00008422697,0.00006123175,0.00004780116],"domain_scores_gemma":[0.9968405,0.001854482,0.0007265137,0.0001414138,0.0003014262,0.0001356501],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001058289,0.000392333,0.9493982,0.0001382721,0.0002593673,0.00005910855,0.0002765385,0.0005102028,0.003869267,0.0001220059,0.0003552776,0.04356126],"study_design_scores_gemma":[0.0000213763,0.001445902,0.9950612,0.00003742944,0.0001228357,0.0001268416,0.000176796,0.0008816396,0.001476356,0.0001837864,0.0004577822,0.000008061212],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963828,0.0007117204,0.0009543091,0.0001118038,0.00001516895,0.00004411927,0.0003187069,0.000009401977,0.001451839],"genre_scores_gemma":[0.99741,0.0006008374,0.0009833883,0.00003640246,0.00002650412,0.00005372715,0.0003355986,0.000002653059,0.0005509123],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001970201,"threshold_uncertainty_score":0.01041955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02818918373296826,"score_gpt":0.2602036569951349,"score_spread":0.2320144732621666,"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."}}