{"id":"W4412532565","doi":"10.1212/nxg.0000000000200266","title":"Combating Genetic Heterogeneity for Polygenic Prediction of Susceptibility to Brain β-Amyloid Deposition","year":2025,"lang":"en","type":"article","venue":"Neurology Genetics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Canadian Institutes of Health Research; GHR Foundation; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; Mayo Foundation for Medical Education and Research; University of Southern California; Mayo Clinic; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association","keywords":"Deposition (geology); Genetic heterogeneity; Biology; Amyloid (mycology); Amyloid β; Genetics; Neuroscience; Medicine; Internal medicine; Pathology; Phenotype; Disease; Gene; Paleontology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002342387,0.0001632588,0.0001964718,0.00007472832,0.00009959014,0.0000120773,0.0002226619,0.0002882342,0.000003089091],"category_scores_gemma":[0.00008614808,0.0001797248,0.0001219688,0.0001227839,0.00008668765,0.000001819153,0.0001649761,0.00009840472,0.000001700487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009762825,"about_ca_system_score_gemma":0.00007265092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006010538,"about_ca_topic_score_gemma":0.0001173151,"domain_scores_codex":[0.9987118,0.00008563617,0.0004727798,0.0003631143,0.00007546721,0.0002912116],"domain_scores_gemma":[0.9991389,0.00005082765,0.0001248676,0.0004789523,0.0001321089,0.00007435034],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004537678,0.0001129996,0.05690997,0.0001260146,0.0001120924,4.755458e-7,0.00005988356,0.006631306,0.9124626,0.0001334456,0.004796609,0.01820083],"study_design_scores_gemma":[0.00276503,0.006061938,0.3036746,0.00002956778,0.0001818226,0.00003684608,0.00003502362,0.03717985,0.62775,0.00195259,0.01984341,0.0004892745],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8565614,0.0004005846,0.1411681,0.0006747771,0.0003778674,0.000582882,0.0001010278,0.00001244871,0.0001208634],"genre_scores_gemma":[0.9867728,0.00005576073,0.009283084,0.003497608,0.0001435448,0.00005355707,0.0001260716,0.00001758424,0.000049947],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2847126,"threshold_uncertainty_score":0.7328963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008841740035772585,"score_gpt":0.2521205977943381,"score_spread":0.2432788577585655,"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."}}