{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003015567,0.000197835,0.0003014004,0.0001097787,0.0002907526,0.00003617883,0.0005288349,0.00002460354,0.00001716204],"category_scores_gemma":[0.0005398644,0.0001559506,0.000009189654,0.0001280912,0.0005219603,0.0002287749,0.0002978724,0.0002406018,0.000001379265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004232529,"about_ca_system_score_gemma":0.000146588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005515299,"about_ca_topic_score_gemma":0.00005546026,"domain_scores_codex":[0.9982585,0.0000285064,0.0003242892,0.0006441359,0.000484557,0.0002600353],"domain_scores_gemma":[0.9970306,0.00006896299,0.0001980802,0.00198858,0.0001321829,0.0005816398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003855536,0.0002003443,0.8899533,0.0002230947,0.0000282405,0.0003396603,0.00004504346,0.00001600637,0.0001224672,0.004200578,0.0009883085,0.1034974],"study_design_scores_gemma":[0.00103114,0.0003486819,0.9657848,0.003675839,0.001070404,0.00006846707,0.0000840089,0.02421836,0.00005388199,0.00283845,0.0005052366,0.0003206905],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07206267,0.000829499,0.9158025,0.005879805,0.00009742629,0.002785948,0.002143757,0.0001830766,0.0002152983],"genre_scores_gemma":[0.7396867,0.0001052894,0.2573706,0.0003424516,0.0002144108,0.000097316,0.002142973,0.00003524837,0.000004970158],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6676241,"threshold_uncertainty_score":0.6359483,"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."}}