{"id":"W2168818001","doi":"10.1016/j.neuroimage.2012.07.059","title":"A computational neurodegenerative disease progression score: Method and results with the Alzheimer's disease neuroimaging initiative cohort","year":2012,"lang":"en","type":"article","venue":"NeuroImage","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":254,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institute of Biomedical Imaging and Bioengineering; Northern California Institute for Research and Education; National Institute on Aging; National Institutes of Health; Pfizer","keywords":"Alzheimer's Disease Neuroimaging Initiative; Disease; Biomarker; Neuroimaging; Population; Oncology; Cohort; Medicine; Neuropsychology; Alzheimer's disease; Imaging biomarker; Asymptomatic; Neuroscience; Bioinformatics; Psychology; Cognition; Internal medicine; Magnetic resonance imaging; Biology","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":[0.0006004623,0.0002490487,0.0002070064,0.000107075,0.0003449439,0.0001112687,0.00009636029,0.0000173426,0.0000327198],"category_scores_gemma":[0.0003268483,0.0001501061,0.00006175812,0.000289039,0.0003545027,0.0003979897,0.0001636368,0.0003656387,0.00001727992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001547138,"about_ca_system_score_gemma":0.0001898089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003544571,"about_ca_topic_score_gemma":4.038407e-7,"domain_scores_codex":[0.997389,0.0006051767,0.0002377215,0.0005125832,0.0007836284,0.0004719037],"domain_scores_gemma":[0.998187,0.0004024089,0.0001217868,0.000289416,0.0002562823,0.0007431227],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004768011,0.0006615538,0.9756993,0.00005880036,0.0001528606,0.0007584807,0.000484908,0.00006813109,0.0004984581,0.0002884036,0.002949255,0.01361182],"study_design_scores_gemma":[0.00202771,0.0003584533,0.9897255,0.00007188871,0.0005799081,0.00006415466,0.0000482714,0.004936496,0.0004114634,0.00009400205,0.001521904,0.0001602339],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9589955,0.001475245,0.00764643,0.02348629,0.0001239816,0.00317787,0.0002284746,0.0001475264,0.004718684],"genre_scores_gemma":[0.993377,0.00003433651,0.001681411,0.004220044,0.0001837136,0.0001739653,0.0001725464,0.00004196124,0.0001150741],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03438145,"threshold_uncertainty_score":0.612115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04365538021930011,"score_gpt":0.3615101160452225,"score_spread":0.3178547358259224,"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."}}