{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010502,0.0008212774,0.0005213778,0.002802131,0.000428765,0.001402536,0.0009550815,0.0006909822,0.001304776],"category_scores_gemma":[0.01506918,0.0002311498,0.0009915758,0.001552797,0.0007425638,0.001782869,0.002189135,0.0004392647,0.0003513818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003695192,"about_ca_system_score_gemma":0.0006010632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001837649,"about_ca_topic_score_gemma":0.002393579,"domain_scores_codex":[0.9962749,0.001674778,0.0009220435,0.0004970506,0.0004723292,0.0001588554],"domain_scores_gemma":[0.9909865,0.00194939,0.004130699,0.0008484079,0.001611097,0.000473851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002089752,0.00003303427,0.9946799,0.00005690938,0.0001635536,0.00008359794,0.0002551964,0.0001166653,0.000444292,0.0002225459,0.0001630149,0.003572245],"study_design_scores_gemma":[0.00002478631,0.0005236767,0.9937161,0.0001587911,0.0001458984,0.001397308,0.0006416652,0.0008529987,0.0006403515,0.000949393,0.0009271854,0.00002192581],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894298,0.001404942,0.005143006,0.0001423605,0.00005225197,0.0002295943,0.001451486,0.00003450214,0.002112087],"genre_scores_gemma":[0.9917258,0.000281695,0.004812943,0.0001306789,0.00004082392,0.0003687041,0.002387809,0.000009728978,0.0002419117],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.010502,"threshold_uncertainty_score":0.0555405,"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."}}