{"id":"W4390556486","doi":"10.14283/jpad.2023.134","title":"Personalized Computational Causal Modeling of the Alzheimer Disease Biomarker Cascade","year":2024,"lang":"en","type":"article","venue":"The Journal of Prevention of Alzheimer s Disease","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; National Institute of General Medical Sciences; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; Biogen; BioClinica; Meso Scale Diagnostics; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Canadian Institutes of Health Research; National Science Foundation","keywords":"Biomarker; Dementia; Disease; Personalized medicine; Neuroimaging; Alzheimer's disease; Cognition; Alzheimer's Disease Neuroimaging Initiative; Medicine; Bioinformatics; Psychology; Neuroscience; Internal medicine; Biology","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":[0.001113264,0.0001466546,0.0002587492,0.0001896335,0.00009052027,0.00002322283,0.0002134525,0.00003025879,0.0007775429],"category_scores_gemma":[0.000107062,0.00008125352,0.0006296117,0.0003013209,0.0002625193,0.0001901765,0.00009162306,0.0002405403,0.000009412034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002257465,"about_ca_system_score_gemma":0.0008827122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009484719,"about_ca_topic_score_gemma":4.983261e-7,"domain_scores_codex":[0.9974633,0.0004116034,0.0006520062,0.0001301944,0.001158094,0.0001848188],"domain_scores_gemma":[0.998534,0.0001274937,0.0002729978,0.0002071754,0.0005449455,0.0003134407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.1162736,0.02756912,0.2202052,0.007818811,0.147337,0.002865167,0.01277344,0.09108722,0.04904896,0.02433516,0.06613353,0.2345528],"study_design_scores_gemma":[0.005853765,0.000673041,0.6575318,0.003773793,0.04160735,0.0002059103,0.0007549589,0.270283,0.002375959,0.01579661,0.0008006057,0.000343214],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9189224,0.06616228,0.007893168,0.005327425,0.0003777143,0.0008330875,0.0001000182,0.00001626627,0.0003676191],"genre_scores_gemma":[0.9990928,0.0002182533,0.0001534494,0.0001262543,0.0001131942,0.00000646927,0.00001369092,0.00002146523,0.0002544058],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4373266,"threshold_uncertainty_score":0.851355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05936578749161757,"score_gpt":0.3670152474254739,"score_spread":0.3076494599338563,"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."}}