{"id":"W3170936042","doi":"10.1002/nbm.4564","title":"MRI of healthy brain aging: A review","year":2021,"lang":"en","type":"review","venue":"NMR in Biomedicine","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":160,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Brain aging; Hyperintensity; Aging brain; White matter; Magnetic resonance imaging; Neuroscience; Healthy aging; Human brain; Brain size; Psychology; Medicine; Cognition; Gerontology; 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":[0.000725863,0.001225994,0.001556049,0.005299222,0.0003224503,0.001035121,0.0008565248,0.001242115,0.004012671],"category_scores_gemma":[0.001844281,0.0004616336,0.0008210834,0.004345306,0.0004434479,0.001871063,0.000637398,0.001106826,0.001807924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006704968,"about_ca_system_score_gemma":0.001668382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001880449,"about_ca_topic_score_gemma":0.002331106,"domain_scores_codex":[0.9997483,0.00004499913,0.00006382955,0.00004970729,0.00007290162,0.00002017166],"domain_scores_gemma":[0.9988991,0.0006509652,0.0001465814,0.0000240658,0.0002309707,0.00004825819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008298226,0.00005083806,0.0003065219,0.0664676,0.0002083239,0.0003514556,0.0001165939,0.0003088971,0.001015901,0.001671079,0.04370854,0.8857113],"study_design_scores_gemma":[0.00002137418,0.000151722,0.002477517,0.03382231,0.0005973419,0.003948899,0.0001250378,0.000126842,0.000502447,0.001868155,0.9563133,0.00004499941],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0000600585,0.9992131,0.00007001863,0.0001621174,0.0001444385,0.000003444499,0.00001739385,0.000005063129,0.0003243173],"genre_scores_gemma":[0.0003550276,0.999054,0.0001344071,0.0001318657,0.0001495899,0.000005028522,0.00002128127,0.00000124846,0.000147582],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.005299222,"threshold_uncertainty_score":0.01342374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2042998129468443,"score_gpt":0.5203831557151053,"score_spread":0.316083342768261,"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."}}