{"id":"W4412672398","doi":"10.1101/2025.07.24.25332172","title":"Intersecting vulnerabilities: Race, Depression, and White Matter Hyperintensity burden in Aging","year":2025,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; McGill University; Douglas Mental Health University Institute","funders":"National Institute on Aging; Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; Compute Canada; Alzheimer Society Research Program; Canadian Institutes of Health Research; Alzheimer Society; Réseau en Bio-Imagerie du Quebec","keywords":"Hyperintensity; Race (biology); Depression (economics); White matter; Psychology; Medicine; Sociology; Magnetic resonance imaging; Economics; Gender studies; 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":[0.001212873,0.0002154123,0.0003661268,0.000447188,0.0003869079,0.0007240719,0.0002675024,0.000309111,0.001397305],"category_scores_gemma":[0.002603601,0.000173888,0.0004329944,0.00050123,0.0003144979,0.0004010002,0.0006245753,0.0003607456,0.00008105022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002662275,"about_ca_system_score_gemma":0.0003197912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004953221,"about_ca_topic_score_gemma":0.006513366,"domain_scores_codex":[0.9996118,0.0001821743,0.00002578236,0.00008654722,0.00004189978,0.00005180341],"domain_scores_gemma":[0.9987978,0.0002977534,0.0005891669,0.0001030214,0.00007325388,0.0001390756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002072624,0.00004303086,0.9912804,0.00002142653,0.0002907636,0.00007634035,0.0001677664,0.0001730125,0.000499601,0.0002271557,0.0002017257,0.006811592],"study_design_scores_gemma":[0.000004393372,0.00002720556,0.998791,0.00001309916,0.00007452649,0.00008783334,0.00008946395,0.000363671,0.00008083072,0.000354319,0.0001108484,0.000002768131],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973481,0.001211875,0.0003189578,0.000287523,0.000007346247,0.00000451343,0.0002174255,0.000004955662,0.0005993448],"genre_scores_gemma":[0.9992815,0.0002565871,0.0002121105,0.00004165382,0.00001276051,0.000004848908,0.00009194035,0.000001114197,0.00009758108],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004953221,"threshold_uncertainty_score":0.009848833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01703114690515051,"score_gpt":0.3112069136097062,"score_spread":0.2941757667045557,"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."}}