{"id":"W4386575300","doi":"10.1016/j.artmed.2023.102657","title":"Walking path images from real-time location data predict degree of cognitive impairment","year":2023,"lang":"en","type":"article","venue":"Artificial Intelligence in Medicine","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"AGE-WELL","keywords":"Apathy; Computer science; Mood; Window (computing); Cognition; Convolutional neural network; Cognitive impairment; Class (philosophy); Artificial intelligence; Spatial cognition; Pattern recognition (psychology); Medicine; Psychiatry","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":[0.000105064,0.0004202937,0.0002123007,0.001420526,0.00007994813,0.0003837931,0.0002163589,0.0004943256,0.002193507],"category_scores_gemma":[0.001604325,0.0001125014,0.0002532641,0.0009066893,0.00009712373,0.0003795399,0.0002095805,0.0002348092,0.0009579403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001331948,"about_ca_system_score_gemma":0.0002197618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007593602,"about_ca_topic_score_gemma":0.01300312,"domain_scores_codex":[0.9999431,0.00000697496,0.000004652644,0.00001434525,0.00001575678,0.00001512931],"domain_scores_gemma":[0.9996672,0.00008127809,0.00006920423,0.00002297009,0.0001155773,0.00004371881],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001552328,0.0003104646,0.8097929,0.0002697371,0.0002697189,0.0008037055,0.0002418152,0.01307857,0.01628022,0.000167587,0.006272129,0.1509607],"study_design_scores_gemma":[0.00003531321,0.0003908434,0.8923246,0.00007671424,0.0001716995,0.001586624,0.0007554388,0.09805206,0.004128239,0.0007066401,0.001729479,0.00004230111],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859308,0.0003812733,0.005674302,0.00009169407,0.00004365196,0.00003527722,0.00635842,0.0002809349,0.001203611],"genre_scores_gemma":[0.9923057,0.0002156475,0.003897283,0.00001888823,0.00001146602,0.00001685942,0.00306391,0.00001229149,0.0004579345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007593602,"threshold_uncertainty_score":0.01509881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1137799594877141,"score_gpt":0.3998518388328737,"score_spread":0.2860718793451595,"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."}}