{"id":"W3038146172","doi":"10.1101/2020.07.07.191809","title":"Beware of White Matter Hyperintensities Causing Systematic Errors in Grey Matter Segmentations!","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; University of Alberta; Centres Intégré Universitaires de Santé et de Services Sociaux","funders":"National Institute on Aging; 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; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; Novartis Pharmaceuticals Corporation; Alzheimer Society; U.S. Department of Defense; Eli Lilly and Company; Consortium canadien en neurodégénérescence associée au vieillissement; Bristol-Myers Squibb; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Sanofi; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Hyperintensity; Putamen; Grey matter; White matter; Fluid-attenuated inversion recovery; Neuroimaging; Alzheimer's Disease Neuroimaging Initiative; Cardiology; Internal medicine; Effects of sleep deprivation on cognitive performance; Psychology; Medicine; Magnetic resonance imaging; Neuroscience; Cognition; Disease; Cognitive impairment; 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":[0.02594097,0.0008139571,0.001005113,0.001595526,0.0009798118,0.002171193,0.001910512,0.00172324,0.019648],"category_scores_gemma":[0.1284187,0.001032629,0.0008339361,0.001358614,0.001966527,0.00213785,0.001556686,0.001698778,0.006945617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006372401,"about_ca_system_score_gemma":0.001340824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002514027,"about_ca_topic_score_gemma":0.005020733,"domain_scores_codex":[0.9879957,0.005444114,0.001482862,0.002160214,0.002663692,0.0002535226],"domain_scores_gemma":[0.8977763,0.05011961,0.01816509,0.0211198,0.01161434,0.001204841],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001981582,0.0002290836,0.1524582,0.003737868,0.00180181,0.003404038,0.005625718,0.004578535,0.02562621,0.01309955,0.295268,0.4921895],"study_design_scores_gemma":[0.0005075507,0.0008184318,0.2757659,0.006010056,0.001096222,0.01461163,0.002822598,0.05154884,0.1106181,0.121187,0.4143423,0.0006714156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2733034,0.01024548,0.5811377,0.04673579,0.01511,0.0008868523,0.008118431,0.03807588,0.02638643],"genre_scores_gemma":[0.6328483,0.002194765,0.3202929,0.01330219,0.002321082,0.001026165,0.002368048,0.008133883,0.01751261],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02594097,"threshold_uncertainty_score":0.1371906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04253700151309517,"score_gpt":0.2850852145106956,"score_spread":0.2425482129976005,"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."}}