{"id":"W4324386779","doi":"10.1101/2023.03.10.23287119","title":"Testing a polygenic risk score for morphological microglial activation in Alzheimer’s disease and aging","year":2023,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Neuroinflammation and Neurodegeneration Mechanisms","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto; Centre for Addiction and Mental Health","funders":"National Institute on Aging; National Institutes of Health; Government of Canada; Alzheimer's Disease Neuroimaging Initiative; Canadian Institutes of Health Research; University of Southern California; National Institute of Biomedical Imaging and Bioengineering; Northern California Institute for Research and Education; Foundation for the National Institutes of Health; U.S. Department of Defense","keywords":"Neuroinflammation; Microglia; Biobank; Alzheimer's Disease Neuroimaging Initiative; Neuroimaging; Dementia; Medicine; Disease; Alzheimer's disease; Population; Neuroscience; Oncology; Internal medicine; Psychology; Bioinformatics; Biology","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":[0.004212715,0.0008606239,0.0005233051,0.0009380177,0.0004607739,0.0008061723,0.0005239082,0.0005294633,0.001733531],"category_scores_gemma":[0.007098565,0.0002002902,0.0008764894,0.001161492,0.000463933,0.0003797618,0.0007890587,0.0006836464,0.0002117315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002532476,"about_ca_system_score_gemma":0.000332104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003207253,"about_ca_topic_score_gemma":0.00315788,"domain_scores_codex":[0.9980561,0.0009860224,0.0001045819,0.0006027299,0.0001583795,0.0000922858],"domain_scores_gemma":[0.996076,0.001677688,0.001042885,0.0007324035,0.0002548867,0.0002159815],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001073198,0.00009154269,0.9763815,0.00004848793,0.001991594,0.000174712,0.00008986802,0.00327619,0.003206762,0.0005233542,0.0007200528,0.0124228],"study_design_scores_gemma":[0.00005300056,0.0004336735,0.9715925,0.00002412684,0.0008583902,0.0004270581,0.0000607615,0.02207714,0.001377786,0.002358379,0.0007095446,0.00002766427],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9878764,0.0003860009,0.00968146,0.0001541453,0.00002339177,0.00002140017,0.001290329,0.00006735381,0.0004993837],"genre_scores_gemma":[0.996953,0.00005261946,0.002287195,0.0000291583,0.0000141993,0.00001526988,0.0005236078,0.000007957862,0.0001169442],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004212715,"threshold_uncertainty_score":0.02227926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.165471360950203,"score_gpt":0.3204803032780152,"score_spread":0.1550089423278121,"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."}}