{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0020521,0.0002129667,0.0005067455,0.0005208171,0.00006703553,0.00001544623,0.0003612429,0.00009309052,0.001564889],"category_scores_gemma":[0.00249524,0.0001742577,0.00004483049,0.00155497,0.0004826387,0.0002108037,0.0002918959,0.000278286,0.0004885096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000747654,"about_ca_system_score_gemma":0.0002016232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002539853,"about_ca_topic_score_gemma":0.0001248304,"domain_scores_codex":[0.9968596,0.0001825704,0.0008860221,0.0006174065,0.0009676383,0.000486778],"domain_scores_gemma":[0.9974831,0.001101471,0.0001718461,0.0005975026,0.0004656046,0.0001804552],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001979474,0.001287302,0.1266731,0.000300537,0.0002939516,0.0007110605,0.005472567,0.00002639368,0.08367907,0.000252198,0.004732239,0.7745921],"study_design_scores_gemma":[0.001244782,0.003499659,0.791181,0.006810094,0.0004836844,0.00001853029,0.02452868,0.06137745,0.1010391,0.009329302,0.00008597574,0.0004018068],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9858029,0.0002580572,0.006205303,0.002092464,0.0002223596,0.001250195,0.0001487634,0.0001391606,0.003880753],"genre_scores_gemma":[0.9961078,0.0009739192,0.00031776,0.00012044,0.0003659712,0.00005748157,0.001752511,0.00002740264,0.0002766687],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7741903,"threshold_uncertainty_score":0.9993478,"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."}}