{"id":"W4206683977","doi":"10.1093/geroni/igab046.1723","title":"Hip Accelerometry Activity Patterns Improve Machine Learning Prediction of 1-Year MoCA Score Change","year":2021,"lang":"en","type":"article","venue":"Innovation in Aging","topic":"Frailty in Older Adults","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Montreal Cognitive Assessment; Quartile; Medicine; Physical therapy; Analysis of variance; Gerontology; Physical medicine and rehabilitation; Demography; Cognition; Cognitive impairment; Internal medicine","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":[0.002383935,0.0008876378,0.0008491133,0.00101204,0.0002502016,0.0006880879,0.0004260212,0.0005057038,0.001490353],"category_scores_gemma":[0.006379374,0.000198267,0.0006832422,0.0004742291,0.0001543362,0.0004365139,0.0004142375,0.0006224256,0.0005315758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002571785,"about_ca_system_score_gemma":0.0005012321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008094756,"about_ca_topic_score_gemma":0.009322023,"domain_scores_codex":[0.9993813,0.0002921476,0.00005063854,0.0001587013,0.00005986655,0.00005730048],"domain_scores_gemma":[0.9979001,0.001197767,0.000346001,0.0001279602,0.0002880094,0.0001400736],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001225616,0.000681147,0.9068001,0.0000604543,0.0008886893,0.00004244269,0.00003095724,0.02055291,0.001072474,0.00006287752,0.001751729,0.06683066],"study_design_scores_gemma":[0.0001159558,0.001217682,0.5751349,0.00007746957,0.0004139987,0.0001051972,0.00007725804,0.4205696,0.001020474,0.0005351612,0.0006996073,0.00003267931],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896294,0.001084095,0.006418932,0.0003954642,0.00008821912,0.00003594095,0.001298291,0.0003037385,0.0007457746],"genre_scores_gemma":[0.9959331,0.0001614525,0.002372787,0.00006135507,0.00006143306,0.0000139525,0.001041228,0.000008879007,0.0003457982],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008094756,"threshold_uncertainty_score":0.01609528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06609770392020337,"score_gpt":0.3047512933431225,"score_spread":0.2386535894229191,"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."}}