{"id":"W2996702412","doi":"10.1101/2019.12.13.19014860","title":"Disease progression modeling in Alzheimer’s disease: insights from the shape of cognitive decline","year":2019,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; F. Hoffmann-La Roche; Biogen; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Cognition; Cognitive decline; Dementia; Disease; Psychology; Timeline; Effects of sleep deprivation on cognitive performance; Cognitive test; Alzheimer's disease; Cognitive psychology; Medicine; Psychiatry; Internal medicine; Statistics","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":[0.004851589,0.0006921837,0.0007909996,0.001175492,0.000364368,0.001559286,0.001331057,0.001155401,0.001476819],"category_scores_gemma":[0.01503955,0.0004167065,0.001726808,0.0008130872,0.001002713,0.001259311,0.001292453,0.001560348,0.000216799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00155339,"about_ca_system_score_gemma":0.001458479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01694166,"about_ca_topic_score_gemma":0.01102945,"domain_scores_codex":[0.9988859,0.0007014017,0.00003651137,0.0001868609,0.0001004408,0.00008890003],"domain_scores_gemma":[0.9938738,0.004953,0.000410956,0.0002874552,0.0002951442,0.0001796152],"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":[0.0001665279,0.0001765591,0.04639443,0.0001178092,0.0002665329,0.0002452454,0.001168938,0.8203185,0.0008735236,0.09567552,0.001355407,0.03324102],"study_design_scores_gemma":[0.000008677537,0.00003271307,0.003048859,0.00001899688,0.00002754249,0.00004175519,0.00006066521,0.9674981,0.00007770884,0.0285057,0.0006676327,0.00001171854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2896676,0.001430515,0.6989274,0.003545834,0.00008643622,0.0001306655,0.0007208104,0.0003320119,0.005158683],"genre_scores_gemma":[0.9622495,0.0004934293,0.03420399,0.0001590492,0.00005663417,0.0001306042,0.0002540855,0.00004508036,0.002407676],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01694166,"threshold_uncertainty_score":0.0336861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06307246842997379,"score_gpt":0.3645322701609642,"score_spread":0.3014598017309905,"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."}}