{"id":"W2564942951","doi":"10.1016/j.trci.2016.12.001","title":"Cross‐validation of optimized composites for preclinical Alzheimer's disease","year":2016,"lang":"en","type":"article","venue":"Alzheimer s & Dementia Translational Research & Clinical Interventions","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute on Aging; Japan Science and Technology Agency; Genentech; H. Lundbeck A/S; Servier; Canadian Institutes of Health Research; Weston Brain Institute; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; Biogen; Takeda Pharmaceutical Company; AbbVie; Norman Cousins Center for Psychoneuroimmunology; Merck; GE Healthcare; BioClinica; Eli Lilly and Company; Michael J. Fox Foundation for Parkinson's Research","keywords":"Weighting; Cognition; Statistical power; Sample size determination; Cross-validation; Face validity; Computer science; Mathematics; Statistics; Artificial intelligence; Medicine; Psychometrics; Psychiatry","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":[0.06478496,0.00226135,0.001378777,0.001536514,0.0006027998,0.001548266,0.001116496,0.001354212,0.001426486],"category_scores_gemma":[0.07656124,0.0006647178,0.002856079,0.0009726195,0.00127723,0.001070808,0.001563184,0.001735017,0.0005032696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008735167,"about_ca_system_score_gemma":0.001279921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001136202,"about_ca_topic_score_gemma":0.001450056,"domain_scores_codex":[0.984781,0.01029263,0.001167343,0.001790114,0.001620167,0.0003486387],"domain_scores_gemma":[0.9433553,0.03940598,0.004590516,0.00705066,0.005203257,0.000394316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.006966067,0.001194566,0.142933,0.001178308,0.008902172,0.0002196939,0.0005468646,0.5873989,0.03307609,0.003661616,0.004620979,0.2093018],"study_design_scores_gemma":[0.000437443,0.002528395,0.1439359,0.0003022883,0.001236045,0.0003497037,0.0000903668,0.7951682,0.04211814,0.009083347,0.004523016,0.0002272181],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5275407,0.002158769,0.4641401,0.0002819019,0.0001533047,0.000865065,0.0010377,0.001741796,0.002080551],"genre_scores_gemma":[0.8514816,0.0001960153,0.1439702,0.000228807,0.00003277532,0.0008347957,0.002387825,0.0002904426,0.0005775439],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06478496,"threshold_uncertainty_score":0.3426197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3548713531046037,"score_gpt":0.5604750576522419,"score_spread":0.2056037045476383,"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."}}