{"id":"W4225979797","doi":"10.21203/rs.3.rs-1503113/v1","title":"Mega-analysis of brain structural covariance, genetics, and clinical phenotypes","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"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; Novartis Pharmaceuticals Corporation; F. Hoffmann-La Roche; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Mega-; Genetics; Phenotype; Statistical genetics; Covariance; Biology; Genotype; Mathematics; Statistics; Gene; Physics; Pharmacogenetics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008229273,0.001452773,0.00270781,0.002337125,0.00116744,0.002239549,0.002395049,0.001261158,0.03638364],"category_scores_gemma":[0.03195276,0.001388107,0.00345479,0.003194034,0.0007269727,0.001108795,0.00247946,0.001926512,0.006431823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004663466,"about_ca_system_score_gemma":0.001697486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00320387,"about_ca_topic_score_gemma":0.004412878,"domain_scores_codex":[0.9952604,0.002837777,0.0002322831,0.001074588,0.0002486539,0.0003462623],"domain_scores_gemma":[0.9835292,0.01320556,0.0003956748,0.002047142,0.0003082301,0.000514109],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.01671362,0.0008960585,0.4003764,0.003488016,0.03041639,0.004096998,0.002960904,0.0524336,0.02266155,0.04046881,0.1802456,0.2452421],"study_design_scores_gemma":[0.005334578,0.002038477,0.3871473,0.0006740986,0.01511628,0.003452757,0.001310453,0.3413872,0.01336769,0.115692,0.1141066,0.0003725853],"study_design_candidate":"meta_analysis","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6408973,0.003404568,0.2038907,0.003107604,0.0008270411,0.0002675896,0.1174261,0.02310187,0.007077202],"genre_scores_gemma":[0.7523497,0.0005914173,0.1809269,0.0003976946,0.000231881,0.0008139809,0.05253002,0.006164954,0.00599339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03638364,"threshold_uncertainty_score":0.1217154,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0818726761744533,"score_gpt":0.4656842169862564,"score_spread":0.3838115408118031,"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."}}