{"id":"W2592343442","doi":"10.1371/journal.pone.0173372","title":"Prediction and classification of Alzheimer disease based on quantification of MRI deformation","year":2017,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Neurological Disease Mechanisms and Treatments","field":"Neuroscience","cited_by":181,"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; Servier; National Natural Science Foundation of China; Eisai; Genentech; Pfizer; Biogen; BioClinica; Alzheimer's Association; Amorfix Life Sciences; F. Hoffmann-La Roche; Medpace; AstraZeneca; Eli Lilly and Company; Bristol-Myers Squibb; Novartis Pharmaceuticals Corporation; Synarc; Bayer HealthCare; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; National Institute on Aging; Foundation for the National Institutes of Health","keywords":"Magnetic resonance imaging; Alzheimer's disease; Disease; Deformation (meteorology); Medicine; Pathology; Radiology; Physics","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":[0.00092578,0.0004473069,0.0004940133,0.001528812,0.0001369248,0.0004481507,0.0002348867,0.0005078887,0.0003938441],"category_scores_gemma":[0.001831655,0.0001191086,0.0003900556,0.0004401086,0.00028601,0.0004661539,0.0002667285,0.0003498209,0.0002437055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002293534,"about_ca_system_score_gemma":0.0002000882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009124107,"about_ca_topic_score_gemma":0.0009636111,"domain_scores_codex":[0.9997084,0.00007635742,0.00003753757,0.00008610136,0.00006413001,0.00002749449],"domain_scores_gemma":[0.999363,0.0002435645,0.0001462075,0.00007423561,0.0001335838,0.00003947777],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008101533,0.0004450598,0.2194995,0.0001860665,0.0002968758,0.0003454876,0.000184659,0.07284565,0.09887094,0.001774872,0.001614107,0.6031267],"study_design_scores_gemma":[0.00002687763,0.0004094073,0.1818113,0.00004103555,0.00008984476,0.0007842915,0.00011806,0.7889823,0.02283998,0.003753221,0.001074553,0.00006910607],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7712149,0.001258634,0.2245757,0.0002724168,0.00009853381,0.00009723254,0.0003586117,0.0005631031,0.001560887],"genre_scores_gemma":[0.9650566,0.0002519036,0.03402897,0.00002296919,0.00003011812,0.00002154283,0.0002036741,0.00001019189,0.0003739805],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001528812,"threshold_uncertainty_score":0.004896045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.185739333691924,"score_gpt":0.2844969217340717,"score_spread":0.09875758804214763,"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."}}