{"id":"W2978584402","doi":"10.3389/fnagi.2019.00270","title":"Free Water in White Matter Differentiates MCI and AD From Control Subjects","year":2019,"lang":"en","type":"article","venue":"Frontiers in Aging Neuroscience","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":100,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","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; BioClinica; F. Hoffmann-La Roche; University of Southern California; Biogen; 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":"White matter; Diffusion MRI; Hyperintensity; Partial volume; Fluid-attenuated inversion recovery; Cardiology; Psychology; Neuroscience; Internal medicine; Magnetic resonance imaging; Pathology; Medicine; Nuclear medicine; Radiology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001070056,0.0007306538,0.0005824282,0.002334363,0.0003737096,0.0008158206,0.0002817776,0.0007647809,0.001196178],"category_scores_gemma":[0.001789531,0.0001874691,0.0003086139,0.0005315107,0.0005955248,0.0004394576,0.0004877648,0.0002064839,0.0002734749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001907568,"about_ca_system_score_gemma":0.0001896943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003390199,"about_ca_topic_score_gemma":0.003731282,"domain_scores_codex":[0.9996644,0.00006273077,0.00005603791,0.0001246375,0.00005283608,0.00003945919],"domain_scores_gemma":[0.9995384,0.0001358094,0.0001242706,0.00006280468,0.00005605083,0.00008274018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0102263,0.0005622788,0.8805643,0.0003236773,0.0007592512,0.00116869,0.001366948,0.0006179926,0.05488272,0.0005014546,0.0007255204,0.04830083],"study_design_scores_gemma":[0.00005812392,0.0005826405,0.9933279,0.00001718014,0.0001175119,0.0006016273,0.0004034565,0.00121524,0.002671629,0.000382535,0.0006039169,0.00001827944],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979724,0.0006381679,0.0005904062,0.0000205612,0.0000130805,0.0000281005,0.0001698437,0.00002495114,0.0005424569],"genre_scores_gemma":[0.9982919,0.000155636,0.0007072696,0.00003301354,0.00002064398,0.00002562014,0.0005013258,0.000008394662,0.0002561818],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003390199,"threshold_uncertainty_score":0.006740928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01591202520934134,"score_gpt":0.2660259573467302,"score_spread":0.2501139321373889,"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."}}