{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005540566,0.0003404325,0.003194959,0.0004573267,0.00002072213,0.000002192167,0.0002233273,0.0001667958,0.000173066],"category_scores_gemma":[0.0004649846,0.0002565649,0.0003440792,0.001984273,0.000170712,0.00002041001,0.0001055289,0.0006370393,0.00001196324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000155547,"about_ca_system_score_gemma":0.0006131329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001954972,"about_ca_topic_score_gemma":0.000001243342,"domain_scores_codex":[0.9974684,0.0001087173,0.001268842,0.0005458117,0.0003227518,0.0002855046],"domain_scores_gemma":[0.9979012,0.0002715336,0.0005126152,0.001000814,0.000109966,0.0002038806],"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":[0.000002100163,0.0001410161,0.000006667559,0.1464064,0.00002548505,0.00006470728,0.000007311869,8.179252e-9,0.000004477173,0.0002643613,0.07889468,0.7741828],"study_design_scores_gemma":[0.0002493865,0.0001354843,0.000003134579,0.2549832,0.0003705844,0.0003346596,0.000002768197,0.000001000418,0.000001390953,0.00003230904,0.743768,0.0001180954],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[1.203992e-7,0.9747193,0.0005857668,0.02185049,0.00007712894,0.001850356,0.0000365688,0.00009076513,0.0007895442],"genre_scores_gemma":[4.16228e-7,0.9854218,0.005690882,0.007208392,0.0002588287,0.0002938721,0.0005647233,0.00006386283,0.0004972305],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.7740647,"threshold_uncertainty_score":0.9999887,"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."}}