{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001731617,0.0007859673,0.0006425532,0.001051461,0.0002111531,0.0008223045,0.0002874981,0.0005418065,0.001426926],"category_scores_gemma":[0.006308652,0.0001649647,0.0005248424,0.0005215153,0.0002441646,0.0003161981,0.0002618763,0.0006592906,0.0009751344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003392349,"about_ca_system_score_gemma":0.0002272316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002031864,"about_ca_topic_score_gemma":0.00247349,"domain_scores_codex":[0.9992929,0.0002473325,0.00005371627,0.000256355,0.00007780955,0.00007198253],"domain_scores_gemma":[0.9975903,0.00136282,0.0004250281,0.0001622882,0.0003706157,0.00008899792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001610205,0.0002103398,0.9085225,0.00011376,0.0003563574,0.0002008725,0.0001546029,0.006043312,0.01214748,0.0002346619,0.003248175,0.06715786],"study_design_scores_gemma":[0.0001174356,0.0007811215,0.8558608,0.00009679959,0.0004927999,0.000675967,0.0002147636,0.1208341,0.01470802,0.002905478,0.00322003,0.00009268809],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.980943,0.001092187,0.01133287,0.0005627738,0.00009451401,0.00006399452,0.0028649,0.000319471,0.002726234],"genre_scores_gemma":[0.9929964,0.0001448772,0.004825334,0.0001227276,0.00004653143,0.00004699124,0.001162459,0.00001376817,0.0006408408],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002031864,"threshold_uncertainty_score":0.009157777,"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."}}