{"id":"W2946009800","doi":"10.1016/j.biopsych.2019.03.795","title":"S44. Using Acoustic and Linguistic Markers From Spontaneous Speech to Predict Scores on the Montreal Cognitive Assessment (MoCA)","year":2019,"lang":"en","type":"article","venue":"Biological Psychiatry","topic":"Delphi Technique in Research","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University; University of Toronto","funders":"","keywords":"Montreal Cognitive Assessment; Construct (python library); Cognition; Psychology; Audiology; Cognitive psychology; Linguistics; Medicine; Cognitive impairment; Computer science; Neuroscience","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001052743,0.0001644014,0.0002056594,0.00006470404,0.0003497484,0.0001132185,0.0003956092,0.0002132952,0.0005073484],"category_scores_gemma":[0.001559943,0.0001034515,0.00006282563,0.000221613,0.0003693953,0.00002428397,0.0001539639,0.0003783181,0.00008082367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001282425,"about_ca_system_score_gemma":0.0002247469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003014043,"about_ca_topic_score_gemma":0.001604662,"domain_scores_codex":[0.9978456,0.0005279728,0.0002043413,0.0004818891,0.0004992075,0.0004409633],"domain_scores_gemma":[0.9973926,0.002025302,0.00006999676,0.0002209136,0.0000963608,0.0001948151],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002362461,0.0007559398,0.9492567,0.00003749568,0.0002366093,0.00053312,0.001778457,0.00007302529,0.002512434,0.01538512,0.008982525,0.01808607],"study_design_scores_gemma":[0.0007614619,0.002360786,0.9242831,0.0005500837,0.00007971293,0.00003477938,0.01094898,0.001874864,0.00003090036,0.05687034,0.00151314,0.0006918628],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9662442,0.00009945391,0.0002639263,0.002473038,0.0008104454,0.001181415,0.00008597612,0.0001178251,0.02872376],"genre_scores_gemma":[0.9955559,0.00005435444,0.002738613,0.0009141169,0.0005281128,0.00002908386,0.000007918517,0.00001204364,0.0001598805],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04148522,"threshold_uncertainty_score":0.5555109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08299054281298997,"score_gpt":0.4103829005493914,"score_spread":0.3273923577364014,"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."}}