{"id":"W3037862395","doi":"10.3390/genes11060706","title":"Predicting Clinical Dementia Rating Using Blood RNA Levels","year":2020,"lang":"en","type":"article","venue":"Genes","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Eisai; Northern California Institute for Research and Education; BioClinica; F. Hoffmann-La Roche; Servier; Brigham Young University; Biogen; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Dementia; Rating system; Medicine; Computational biology; Biology; Internal medicine; Economics; Disease","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":[0.0004616815,0.000105445,0.0002125907,0.00003602727,0.0001173787,0.00003796196,0.00007726951,0.00005391157,0.0005043543],"category_scores_gemma":[0.0003436402,0.00009344706,0.0001121247,0.000166936,0.00005261552,0.00007570266,0.0001274821,0.0001884681,0.0000503384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007490269,"about_ca_system_score_gemma":0.000132832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001176605,"about_ca_topic_score_gemma":0.000001462331,"domain_scores_codex":[0.9985902,0.0000981215,0.0003676367,0.0002878976,0.0003532619,0.000302835],"domain_scores_gemma":[0.999371,0.00007235159,0.00007384012,0.0001103273,0.0001259252,0.000246549],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004404396,0.00009472349,0.8642039,0.00007089746,0.0002966383,0.00008002512,0.0001479931,0.000003650087,0.1078745,0.000005230936,0.00008102757,0.02709733],"study_design_scores_gemma":[0.006025695,0.001619866,0.7591442,0.0002182666,0.001620718,0.00007848951,0.001388273,0.01830199,0.2089752,0.00003746597,0.002274661,0.0003152168],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935123,0.0008448237,0.002360387,0.001078621,0.0001135463,0.0002980402,0.00000544454,0.00007242981,0.001714413],"genre_scores_gemma":[0.9932538,0.00004810041,0.004480737,0.001182973,0.000908913,0.000007099414,0.00000776482,0.00001906061,0.00009160609],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1050598,"threshold_uncertainty_score":0.5522326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1688714932667869,"score_gpt":0.4137641733116814,"score_spread":0.2448926800448945,"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."}}