{"id":"W2792036635","doi":"10.1016/j.nicl.2018.02.008","title":"Defining SNAP by cross-sectional and longitudinal definitions of neurodegeneration","year":2018,"lang":"en","type":"article","venue":"NeuroImage Clinical","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":19,"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; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; National Institute on Aging; Alzheimer's Association","keywords":"Neurodegeneration; Dementia; Biomarker; Atrophy; Cognitive decline; Cognition; Neuroscience; Cross-sectional study; Alzheimer's disease; Psychology; Medicine; Snap; Neuroimaging; Hyperintensity; Internal medicine; Disease; Pathology; Magnetic resonance imaging; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005605476,0.00009623758,0.0001935107,0.00007009343,0.0001452188,0.00004935573,0.00005352323,0.0000753227,0.0004061476],"category_scores_gemma":[0.001085151,0.00008944691,0.00009626163,0.0001310099,0.0007450292,0.0001123432,0.0000794661,0.000278084,0.00008345105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006935157,"about_ca_system_score_gemma":0.00008575259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006521018,"about_ca_topic_score_gemma":0.000003067984,"domain_scores_codex":[0.9984695,0.0001265358,0.0004912713,0.0003939065,0.0003216153,0.0001971371],"domain_scores_gemma":[0.9988728,0.0003448104,0.0000873172,0.0001758249,0.0003641323,0.0001551558],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002407171,0.0003327802,0.9878568,0.00001908255,0.00002098571,0.00001991278,0.000006125887,7.836935e-8,0.007235476,0.0007708195,0.002114514,0.001382695],"study_design_scores_gemma":[0.001519105,0.002055791,0.9906157,0.00001293317,0.00004003729,0.00009733001,0.000003955389,0.0002105483,0.004277035,0.0002221917,0.0008814028,0.00006403065],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920568,0.00007191261,0.0007769031,0.0003379903,0.0002159135,0.0001612595,0.00003916575,0.00002869638,0.006311312],"genre_scores_gemma":[0.9978657,0.0001642797,0.0006561828,0.0006609433,0.0002581436,0.00001023006,0.0000720752,0.00001440804,0.00029809],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006013222,"threshold_uncertainty_score":0.4447031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1206813040054596,"score_gpt":0.4414282237660978,"score_spread":0.3207469197606382,"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."}}