{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006010189,0.00006066334,0.0000931309,0.00004820559,0.000131889,0.00002019106,0.00008124705,0.00002653436,0.00001481066],"category_scores_gemma":[0.000294047,0.00004985366,0.00002450621,0.00003144142,0.00007642451,0.0001744904,0.00001529098,0.00003138655,0.00001062601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005575827,"about_ca_system_score_gemma":0.00001186159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002464958,"about_ca_topic_score_gemma":2.336922e-7,"domain_scores_codex":[0.9992911,0.0000486184,0.0001595256,0.0001841787,0.0002542418,0.00006238781],"domain_scores_gemma":[0.99923,0.00004089807,0.0002677779,0.0003572131,0.00004132302,0.00006277049],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002763705,0.002492748,0.01772334,0.00007112706,0.00001312962,0.00000117457,0.00001281121,0.00004165123,0.9714824,0.006650691,0.00000500291,0.001229578],"study_design_scores_gemma":[0.0002711125,0.0001470896,0.4938119,0.0000365958,0.0001074184,3.910086e-8,0.000001010487,0.05878691,0.4460462,0.0007629708,0.000001225383,0.00002756027],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980688,0.00001042182,0.0005252731,0.0003906763,0.00002471844,0.0003293456,0.0001328423,0.00002229448,0.0004956314],"genre_scores_gemma":[0.9997083,0.00003111495,0.0001315051,0.00005270016,0.000007901792,0.00003338638,0.00001981437,0.000005041026,0.00001026387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5254362,"threshold_uncertainty_score":0.2032973,"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."}}