{"id":"W2800289050","doi":"10.1109/jbhi.2018.2834317","title":"Early Detection of Mild Cognitive Impairment With In-Home Monitoring Sensor Technologies Using Functional Measures: A Systematic Review","year":2018,"lang":"en","type":"review","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":92,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health and Social Services Centre University Institute of Geriatrics of Sherbrooke; Université Laval; Université de Sherbrooke; Université de Montréal; Institut Universitaire de Gériatrie de Montréal","funders":"Fonds de Recherche du Québec - Santé; Réseau québécois de recherche sur le vieillissement","keywords":"CINAHL; Activities of daily living; Novelty; Dementia; Quality of life (healthcare); Cognition; Systematic review; MEDLINE; Medicine; Psychological intervention; Population; Gerontology; Physical medicine and rehabilitation; Computer science; Psychology; Physical therapy; Disease; Psychiatry; Pathology; Nursing","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":[0.004796389,0.001455084,0.007307536,0.009116174,0.0005830647,0.002267536,0.001984832,0.001796682,0.004127785],"category_scores_gemma":[0.02962084,0.0008581189,0.006711259,0.009100749,0.0006906393,0.002085675,0.001169882,0.000882431,0.0003190752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003191332,"about_ca_system_score_gemma":0.009394032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01103353,"about_ca_topic_score_gemma":0.02972322,"domain_scores_codex":[0.995308,0.001278961,0.001951597,0.0003784684,0.0009350413,0.0001480034],"domain_scores_gemma":[0.9794009,0.01539639,0.003019144,0.000248649,0.00175185,0.0001830756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0001355833,0.00001692195,0.0006720518,0.9638426,0.00483066,0.0000693355,0.0001457543,0.00005181531,0.00008408346,0.00008834532,0.0006831314,0.02937966],"study_design_scores_gemma":[0.0001459555,0.0002029814,0.00445341,0.9242169,0.05940484,0.000328678,0.0002423057,0.00008949865,0.0001675768,0.0001462597,0.01056694,0.00003481283],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001082674,0.9978148,0.0001341369,0.0001046251,0.00006457525,0.0002656959,0.0002961989,0.000007377388,0.0002298753],"genre_scores_gemma":[0.01229303,0.9859427,0.0005714118,0.0002922536,0.00005365856,0.0005329505,0.0002171887,0.00000395725,0.00009296346],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01103353,"threshold_uncertainty_score":0.02536601,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1126356386984051,"score_gpt":0.3997860988166428,"score_spread":0.2871504601182376,"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."}}