{"id":"W2901244611","doi":"10.3389/fnagi.2018.00365","title":"Multiplex Networks for Early Diagnosis of Alzheimer's Disease","year":2018,"lang":"en","type":"article","venue":"Frontiers in Aging Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute on Aging; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Synarc; Meso Scale Diagnostics; University of California, San Diego; BioClinica; Bristol-Myers Squibb; Eli Lilly and Company; Medpace; Biogen; Foundation for the National Institutes of Health","keywords":"Multiplex; Disease; Alzheimer's disease; Neuroscience; Medicine; Psychology; Bioinformatics; Biology; Internal medicine","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0003227461,0.0001725307,0.0002337872,0.0002436352,0.0003284639,0.00004956389,0.0004891793,0.00003232266,0.000003106282],"category_scores_gemma":[0.01036476,0.0001736391,0.00009100408,0.0008787244,0.001160045,0.0003698807,0.0001832354,0.0001255505,0.000002053733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000347998,"about_ca_system_score_gemma":0.00005005162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002442598,"about_ca_topic_score_gemma":0.000006982843,"domain_scores_codex":[0.9979815,0.0001072857,0.0002649114,0.0008224156,0.0003426082,0.0004813123],"domain_scores_gemma":[0.9979347,0.001381228,0.0001402012,0.0003520806,0.00006895594,0.0001228355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000196495,0.0002358417,0.9511843,0.00002591669,0.000004768755,0.00001938672,0.0004379751,0.00279644,0.01321023,0.0005717349,0.02481609,0.006500853],"study_design_scores_gemma":[0.0009959682,0.0004389492,0.6810027,0.000106601,0.00003803419,0.000003006354,0.00005694736,0.2260427,0.07890166,0.002185178,0.009733072,0.0004952275],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.831978,0.0006622105,0.1462312,0.004503753,0.01433919,0.001574239,0.0000995042,0.0002189904,0.0003928484],"genre_scores_gemma":[0.994589,0.00004968287,0.001794962,0.003127647,0.0001715956,0.0001744335,3.888265e-7,0.00002121203,0.0000710267],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2701816,"threshold_uncertainty_score":0.9979714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04590309089801075,"score_gpt":0.2814147756957315,"score_spread":0.2355116847977207,"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."}}