{"id":"W2994407421","doi":"10.1016/j.dadm.2019.08.003","title":"Nonlinear Z‐score modeling for improved detection of cognitive abnormality","year":2019,"lang":"en","type":"article","venue":"Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring","topic":"Psychometric Methodologies and Testing","field":"Decision Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Toronto","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke; National Cancer Institute; National Institute on Aging; Avid Radiopharmaceuticals; National Institutes of Health; Rainwater Charitable Foundation; Biogen; Centers for Disease Control and Prevention; Bristol-Myers Squibb; Eli Lilly and Company; AstraZeneca; Association for Frontotemporal Degeneration; National Heart, Lung, and Blood Institute; Pfizer; U.S. Department of Defense","keywords":"Normative; Standard deviation; Cognition; Residual; Nonlinear system; Mathematics; Statistics; Abnormality; Variance (accounting); Standard score; Psychology; Econometrics; Developmental psychology; Algorithm; Social psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041536,0.001186781,0.0006016833,0.0009403559,0.0003328717,0.001262444,0.001417152,0.0005728023,0.005131265],"category_scores_gemma":[0.01645875,0.0003891989,0.001048534,0.001109186,0.0005540386,0.0008829398,0.00125182,0.001220351,0.001368269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001305112,"about_ca_system_score_gemma":0.001802787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01293502,"about_ca_topic_score_gemma":0.01335434,"domain_scores_codex":[0.9986629,0.0006534766,0.00005792751,0.0002661214,0.0002824151,0.00007710903],"domain_scores_gemma":[0.9945216,0.003553061,0.000743458,0.0004413312,0.0006538975,0.00008650462],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008743214,0.0003504353,0.09055285,0.0003471256,0.0006228755,0.0004286159,0.0005179528,0.483454,0.009382914,0.05111175,0.008325089,0.354032],"study_design_scores_gemma":[0.00002490449,0.00009704063,0.01047307,0.00003706722,0.00005545013,0.0001073687,0.00004079616,0.9724214,0.00248256,0.01073138,0.003489836,0.0000390637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07670768,0.0002396148,0.9171034,0.0005127655,0.00006402474,0.0002204504,0.0008735281,0.001074485,0.003204128],"genre_scores_gemma":[0.6364084,0.0003935747,0.3532563,0.0001457538,0.00005306924,0.000467195,0.001708534,0.0003443719,0.007222721],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01293502,"threshold_uncertainty_score":0.02571946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2880534394599593,"score_gpt":0.458652965280045,"score_spread":0.1705995258200857,"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."}}