{"id":"W4243581773","doi":"10.26226/morressier.58e389aed462b802923859c4","title":"THE FUNCTIONAL NEUROANATOMY UNDERLYING THE MONTREAL COGNITIVE ASSESSMENT: HIGH-DIMENSIONAL MULTIVARIATE MODELLING OF THE RELATIONSHIP BETWEEN COGNITIVE PERFORMANCE AND INFARCT ANATOMY IN ACUTE ISCHAEMIC STROKE","year":2017,"lang":"en","type":"preprint","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Multivariate statistics; Neuroanatomy; Cognition; Montreal Cognitive Assessment; Stroke (engine); Multivariate analysis; Ischaemic stroke; Medicine; Psychology; Neuroscience; Cardiology; Cognitive impairment; Internal medicine; Computer science; Engineering; Ischemia; Machine learning","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":[0.001367914,0.0006958154,0.0003224519,0.0005616005,0.0002856791,0.001149331,0.0007663706,0.0006293155,0.0009475392],"category_scores_gemma":[0.006531943,0.0003205408,0.0008098967,0.0006480989,0.0006561987,0.0005944477,0.0005506237,0.0008274962,0.000217114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004205576,"about_ca_system_score_gemma":0.001183532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04699173,"about_ca_topic_score_gemma":0.03887051,"domain_scores_codex":[0.9997408,0.0001238013,0.000009032071,0.00005939728,0.0000241561,0.0000427956],"domain_scores_gemma":[0.9988866,0.0007383101,0.0001285336,0.0001001636,0.00008536127,0.00006108062],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009824475,0.000352652,0.2000397,0.0001926809,0.0009736528,0.0006276807,0.000871628,0.6444982,0.01518567,0.01449493,0.004590373,0.1171904],"study_design_scores_gemma":[0.00001500343,0.00006214025,0.1452372,0.00001786496,0.00006670752,0.00013453,0.00008955769,0.8415334,0.0009010473,0.01136361,0.0005321917,0.00004666814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9009694,0.0003560872,0.09455997,0.00129925,0.00003711111,0.00003651795,0.001505246,0.0002201283,0.001016215],"genre_scores_gemma":[0.9952136,0.0001176173,0.003483032,0.00002000648,0.000026172,0.00001641592,0.0003485022,0.00003138748,0.0007431826],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04699173,"threshold_uncertainty_score":0.09343642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1120693458674502,"score_gpt":0.339766214673322,"score_spread":0.2276968688058717,"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."}}