{"id":"W2981267655","doi":"10.1016/j.jalz.2019.06.4074","title":"P4‐402: USING 3D SKELETON MOVEMENT DATA AND MACHINE LEARNING TO EVALUATE DISEASE PROGRESSION AND THE IMPACT OF TREATMENTS OR INTERVENTIONS IN PEOPLE WITH DEMENTIA, MCI OR PHYSICAL IMPAIRMENT","year":2019,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Shores Centre for Mental Health Sciences; Memorial University of Newfoundland","funders":"","keywords":"Physical medicine and rehabilitation; Dementia; Computer science; Session (web analytics); Psychological intervention; Artificial intelligence; Psychology; Medicine; Disease","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002986768,0.001355314,0.0007180602,0.001298597,0.00050405,0.001074595,0.0007639058,0.001210146,0.009967365],"category_scores_gemma":[0.007819794,0.0003030017,0.001268714,0.00072359,0.0004781917,0.001299252,0.001459692,0.0007998336,0.002597554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005197431,"about_ca_system_score_gemma":0.0008020052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003083056,"about_ca_topic_score_gemma":0.005692102,"domain_scores_codex":[0.9990801,0.0002917431,0.00007229256,0.0001902989,0.0002892028,0.00007639098],"domain_scores_gemma":[0.9973176,0.0007769498,0.0004801265,0.0002315897,0.0008269034,0.0003668798],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01238542,0.002959983,0.3465545,0.002186355,0.00135989,0.0004085172,0.0007507792,0.01264237,0.01470465,0.001468227,0.03870559,0.5658736],"study_design_scores_gemma":[0.001278041,0.01627832,0.7781177,0.001016289,0.001079959,0.001306772,0.0009590718,0.1134556,0.03941505,0.01290174,0.0338558,0.0003357103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8784872,0.002308202,0.05626921,0.001580468,0.0003957813,0.003410338,0.03453838,0.001910427,0.02110005],"genre_scores_gemma":[0.9400442,0.0006489635,0.03731016,0.0003179139,0.00009650319,0.003589879,0.0110604,0.0001632848,0.006768788],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.009967365,"threshold_uncertainty_score":0.03334415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04648970548714802,"score_gpt":0.3800454056537065,"score_spread":0.3335557001665585,"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."}}