{"id":"W2896426822","doi":"10.1101/443325","title":"Identification of a simple and novel cut-point based CSF and MRI signature for predicting Alzheimer’s disease progression that reinforces the 2018 NIA-AA research framework","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Alzheimer's disease research and treatments","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; National Institutes of Health; Servier; Eisai; University of California, San Diego; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Cut-point; Neuroimaging; Alzheimer's Disease Neuroimaging Initiative; Multivariate statistics; Oncology; Cerebrospinal fluid; Cognitive impairment; Disease; Medicine; Internal medicine; Pathology; Psychology; Computer science; Neuroscience; Machine learning; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.007515107,0.0009998094,0.001527265,0.003090182,0.0004458423,0.003006562,0.0008055868,0.001566985,0.0005514107],"category_scores_gemma":[0.01388718,0.0003076885,0.0008085974,0.001315498,0.0006328591,0.001270749,0.001039765,0.001170192,0.0006181564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007143387,"about_ca_system_score_gemma":0.001695164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001891377,"about_ca_topic_score_gemma":0.001950537,"domain_scores_codex":[0.9971807,0.0008213844,0.0003323609,0.0006841509,0.0008311205,0.0001502431],"domain_scores_gemma":[0.9926198,0.002312614,0.001447705,0.000610163,0.002531145,0.0004784589],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003839206,0.001049962,0.5901277,0.0004627167,0.000865764,0.0005235708,0.0002614846,0.01405157,0.06961712,0.002370503,0.004539594,0.3122908],"study_design_scores_gemma":[0.0003343843,0.004313487,0.4852673,0.0002574824,0.0008456503,0.003437924,0.0003216317,0.4343193,0.05362825,0.01098377,0.006027045,0.0002637524],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8180838,0.002685294,0.1723787,0.001510935,0.0001991517,0.0003258753,0.00168055,0.0008582789,0.002277481],"genre_scores_gemma":[0.8813592,0.0003801147,0.115673,0.000249553,0.0001245645,0.0001108602,0.001550007,0.00004173322,0.0005110709],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007515107,"threshold_uncertainty_score":0.03974414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05355092068102722,"score_gpt":0.3447264868188604,"score_spread":0.2911755661378331,"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."}}