{"id":"W3020859706","doi":"10.1101/2020.05.04.077040","title":"Cognitive and Motor Correlates of Grey and White Matter Pathology in Parkinson’s Disease","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Parkinson's Disease Mechanisms and Treatments","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Université Laval","funders":"Canadian Institutes of Health Research; Consortium canadien en neurodégénérescence associée au vieillissement","keywords":"Atrophy; Grey matter; Hyperintensity; Psychology; Rating scale; Gait; Parkinson's disease; Substantia nigra; Cognition; Fluid-attenuated inversion recovery; Cardiology; White matter; Cognitive decline; Dementia; Neuroscience; Neuroimaging; Internal medicine; Audiology; Physical medicine and rehabilitation; Medicine; Disease; Magnetic resonance imaging; Radiology; Developmental psychology","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":[0.0004955849,0.000305899,0.0003074897,0.001086501,0.0003710213,0.0005107076,0.0002225258,0.0002993263,0.001145365],"category_scores_gemma":[0.00136404,0.0001911311,0.0002623608,0.0005601213,0.0003318312,0.0002664751,0.000352427,0.0001991997,0.0001347572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002212366,"about_ca_system_score_gemma":0.0001388548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004022161,"about_ca_topic_score_gemma":0.004960833,"domain_scores_codex":[0.9998622,0.00002605477,0.00001930646,0.00003759071,0.00003501328,0.00001983084],"domain_scores_gemma":[0.9991282,0.0001640285,0.0004361345,0.00004176551,0.00011353,0.0001163982],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005846995,0.00005081091,0.9951124,0.00002395107,0.0001093538,0.0002369239,0.00008923105,0.0001103844,0.000978179,0.00001126635,0.00004950189,0.002643305],"study_design_scores_gemma":[0.000002990596,0.0000548986,0.9996468,0.000001373517,0.00001105029,0.0001412213,0.00002035528,0.00004786249,0.00003891512,0.00001384116,0.00001994171,8.759285e-7],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994026,0.0002116348,0.0000223384,0.00001143107,0.000001459671,0.000002832644,0.0001187978,0.000002073739,0.0002267645],"genre_scores_gemma":[0.999599,0.00005966982,0.00003925427,0.000006155146,0.000005673324,0.000002835417,0.0001774001,6.675097e-7,0.0001092597],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004022161,"threshold_uncertainty_score":0.007997453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01411133101054892,"score_gpt":0.230629676586704,"score_spread":0.216518345576155,"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."}}