{"id":"W4387771064","doi":"10.1371/journal.pone.0288039","title":"Identification of preclinical dementia according to ATN classification for stratified trial recruitment: A machine learning approach","year":2023,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":6,"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; National Institutes of Health; Genentech; Takeda Pharmaceutical Company; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Merck; Alzheimer's Drug Discovery Foundation; AbbVie; Alzheimer's Association; Foundation for the National Institutes of Health; GE Healthcare; Alzheimer's Disease Neuroimaging Initiative","keywords":"Dementia; Logistic regression; Random forest; Cohort; Medicine; Artificial intelligence; Machine learning; Neurodegeneration; Support vector machine; Computer science; Disease; Pathology","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":[0.0311531,0.0008407812,0.00193954,0.001699602,0.0004917413,0.001420498,0.001127986,0.001063067,0.002553867],"category_scores_gemma":[0.04490965,0.0003408244,0.001909488,0.0008045722,0.0004408459,0.0008406097,0.001114774,0.001487587,0.0006376587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007325023,"about_ca_system_score_gemma":0.002093537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008846283,"about_ca_topic_score_gemma":0.001577883,"domain_scores_codex":[0.9867014,0.01085557,0.0007198432,0.0009364981,0.0004725734,0.0003140273],"domain_scores_gemma":[0.9727958,0.02118153,0.002356377,0.001763378,0.001237461,0.0006654372],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01125375,0.001214408,0.5160994,0.0007182341,0.002911619,0.0004122597,0.0005067415,0.08847915,0.004351444,0.003218877,0.006510526,0.3643236],"study_design_scores_gemma":[0.0009917228,0.002106061,0.09023544,0.0002629884,0.0008845546,0.0004073461,0.000134624,0.8803734,0.002956611,0.01968357,0.001862898,0.0001008114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5994384,0.001958747,0.3894023,0.00316093,0.0002119609,0.001473301,0.001810441,0.0009864031,0.00155749],"genre_scores_gemma":[0.9227754,0.0001127322,0.07471193,0.0004426036,0.0001211485,0.0005454965,0.0009525045,0.00003706751,0.0003011741],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0311531,"threshold_uncertainty_score":0.1647553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3836364958135712,"score_gpt":0.4257435355017182,"score_spread":0.042107039688147,"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."}}