{"id":"W2609432052","doi":"10.1002/sim.7300","title":"Constructing longitudinal disease progression curves using sparse, short‐term individual data with an application to Alzheimer's disease","year":2017,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":34,"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; Genentech; National Institutes of Health; IXICO; H. Lundbeck A/S; Servier; Eisai; Department of Health, Government of Western Australia; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; Eli Lilly and Company; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; University of Southern California; F. Hoffmann-La Roche; Australian Government; Novartis Pharmaceuticals Corporation; Government of Western Australia; Bristol-Myers Squibb; Alzheimer's Drug Discovery Foundation; Australian Institute of Health and Welfare, Australian Government; Foundation for the National Institutes of Health","keywords":"Term (time); Trajectory; Construct (python library); Regression; Computer science; Longitudinal data; Disease; Regression analysis; Statistics; Algorithm; Mathematics; Applied mathematics; Machine learning; Medicine; Data mining; 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":[0.01108801,0.0006037286,0.0007400894,0.001697061,0.0005732649,0.001172537,0.00125997,0.001197755,0.00128266],"category_scores_gemma":[0.04872779,0.0005450719,0.001642871,0.002102704,0.00083953,0.001516317,0.001583755,0.001818081,0.0004113617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008395015,"about_ca_system_score_gemma":0.001506784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01104951,"about_ca_topic_score_gemma":0.01039852,"domain_scores_codex":[0.9971283,0.001879202,0.0001286543,0.0004058829,0.000359966,0.00009806604],"domain_scores_gemma":[0.9720529,0.02044667,0.002528398,0.003106872,0.001562806,0.0003022052],"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":[0.0002586326,0.0001443828,0.03087656,0.0003183796,0.0002768984,0.0002616606,0.001489893,0.771735,0.003596848,0.02528835,0.0008538575,0.1648996],"study_design_scores_gemma":[0.00002065085,0.0001605863,0.01022396,0.00006936382,0.00003444411,0.0001455811,0.0001839329,0.9625807,0.00127515,0.02288329,0.002340261,0.00008209865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09368449,0.000280232,0.904322,0.0003072903,0.00002140177,0.0001151073,0.0003334775,0.0005167742,0.0004192643],"genre_scores_gemma":[0.5495142,0.0005056954,0.4476606,0.00007661182,0.00002775847,0.0002471636,0.001002513,0.0001613978,0.0008041302],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01108801,"threshold_uncertainty_score":0.05863971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2365476554198065,"score_gpt":0.4887127165419818,"score_spread":0.2521650611221753,"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."}}