{"id":"W2559177759","doi":"10.1038/srep34567","title":"COMPASS: A computational model to predict changes in MMSE scores 24-months after initial assessment of Alzheimer’s disease","year":2016,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; National Institutes of Health; Genentech; National Institute of Neurological Disorders and Stroke; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; University of California, San Diego; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Foundation for the National Institutes of Health; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Science Foundation","keywords":"Compass; Recall; Disease; Dementia; Correlation; Psychology; Medicine; Computer science; Internal medicine; Cognitive psychology; Cartography; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0007772497,0.0009032941,0.0006726886,0.0007810623,0.0003759622,0.0007325968,0.001420242,0.0008714205,0.002406663],"category_scores_gemma":[0.003688757,0.0003352446,0.001097487,0.0005590205,0.0002409064,0.0005022357,0.0007149326,0.0009430224,0.0004865707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007019747,"about_ca_system_score_gemma":0.001576663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03680703,"about_ca_topic_score_gemma":0.04744419,"domain_scores_codex":[0.9998134,0.00005950569,0.0000121308,0.00006211664,0.00002905884,0.00002383213],"domain_scores_gemma":[0.9991731,0.0005485678,0.00005583416,0.00005142673,0.0001095265,0.00006139228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004513341,0.0002552736,0.03303252,0.000127584,0.0003642877,0.0002531723,0.00008756973,0.8897091,0.0004899143,0.003413598,0.01653127,0.0552844],"study_design_scores_gemma":[0.00002122663,0.00002850168,0.0006995951,0.000007476098,0.0000191631,0.00002318654,0.000008610585,0.9961367,0.00008826004,0.002130898,0.0008300335,0.000006246478],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4147651,0.003146989,0.5228574,0.008404552,0.001084198,0.0005130282,0.02776064,0.009161343,0.01230663],"genre_scores_gemma":[0.8628451,0.0008776209,0.1137873,0.001151318,0.0002879833,0.0006501822,0.01398632,0.0002628191,0.006151233],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03680703,"threshold_uncertainty_score":0.07318556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04654628169920928,"score_gpt":0.3722781818283559,"score_spread":0.3257319001291467,"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."}}