{"id":"W2838664376","doi":"10.1093/gigascience/giy085","title":"A similarity-based approach to leverage multi-cohort medical data on the diagnosis and prognosis of Alzheimer's disease","year":2018,"lang":"en","type":"article","venue":"GigaScience","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Sanofi Genzyme; Genentech; National Institutes of Health; IXICO; H. Lundbeck A/S; Servier; Eisai; F. Hoffmann-La Roche; National Institute of Environmental Health Sciences; Northern California Institute for Research and Education; Meso Scale Diagnostics; Teva Pharmaceutical Industries; University of Southern California; Pfizer; Biogen; GlaxoSmithKline; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Michael J. Fox Foundation for Parkinson's Research; Foundation for the National Institutes of Health; Alzheimer's Disease Neuroimaging Initiative; Sanofi; Alzheimer's Association; National Science Foundation","keywords":"Disease; Similarity (geometry); Cohort; Medicine; Leverage (statistics); Correlation; Receiver operating characteristic; Alzheimer's disease; Semantic similarity; Computer science; Artificial intelligence; Machine learning; Pathology; Mathematics","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":[0.001734553,0.000113366,0.0001642216,0.0001005888,0.0001836166,0.00003993415,0.0005682061,0.00003482393,0.0001784255],"category_scores_gemma":[0.001777084,0.00006874452,0.00003444689,0.0004700433,0.000929349,0.00009606706,0.0004306097,0.0001485146,0.00002218121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001162782,"about_ca_system_score_gemma":0.0003465723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000489054,"about_ca_topic_score_gemma":0.000007399466,"domain_scores_codex":[0.9975479,0.0001041182,0.0001755999,0.0005509974,0.0013049,0.0003165276],"domain_scores_gemma":[0.9984074,0.0001799511,0.00003735563,0.0006666971,0.0001668324,0.0005417414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001404999,0.001684345,0.9830443,0.00005709184,0.00005717814,0.00002295851,0.0001314148,7.078495e-7,0.0002196894,0.0001664579,0.002554315,0.01192108],"study_design_scores_gemma":[0.0007097315,0.0005816134,0.9738494,0.0001969255,0.000146373,0.000002973502,0.00008060998,0.01679508,0.006464204,0.00001495643,0.001060375,0.00009772302],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9791939,0.0001748174,0.005029584,0.0117788,0.00005180737,0.001829433,0.00007031942,0.00002970174,0.001841593],"genre_scores_gemma":[0.9932717,0.00002880453,0.002011796,0.004315792,0.00005579529,0.0002527498,0.00002000132,0.000008044932,0.00003530479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01679438,"threshold_uncertainty_score":0.3424225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1383658824148157,"score_gpt":0.3822898632272574,"score_spread":0.2439239808124417,"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."}}