{"id":"W4366564131","doi":"10.48550/arxiv.2304.09754","title":"Joint Modeling of Biomarker Cascades Along An Unobserved Disease Progression with Differentiate Covariate Effects: An Application in Alzheimer's Disease","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; Johns Hopkins University; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Biomarker; Leverage (statistics); Computer science; Disease; Medicine; Artificial intelligence; Internal medicine; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006864402,0.0009762341,0.001143969,0.001060546,0.0005913452,0.001285579,0.001414345,0.001517339,0.001282514],"category_scores_gemma":[0.01317562,0.0007178998,0.001360217,0.001267665,0.001110914,0.001644749,0.001427732,0.002127527,0.0002110706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001036172,"about_ca_system_score_gemma":0.00131586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01461866,"about_ca_topic_score_gemma":0.01113801,"domain_scores_codex":[0.9988567,0.0005658648,0.00004489221,0.0003461576,0.00008966458,0.00009671356],"domain_scores_gemma":[0.9933572,0.004971266,0.0006612709,0.0003640406,0.0003653891,0.0002807834],"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":[0.0003684762,0.0001332791,0.02583097,0.00008822932,0.0002373527,0.0004992148,0.0002904165,0.9055986,0.001353542,0.03259904,0.001225122,0.03177582],"study_design_scores_gemma":[0.00001339475,0.00002959439,0.001765756,0.000007491295,0.00002766256,0.00004116764,0.00001399693,0.9816037,0.0001360684,0.01601577,0.0003336077,0.00001165372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2451674,0.001677615,0.7479517,0.002459286,0.00009713732,0.0001208504,0.0009758268,0.0004404495,0.001109832],"genre_scores_gemma":[0.9280495,0.0008896037,0.06651644,0.0001857348,0.000132373,0.0001491913,0.0007854499,0.00005024205,0.00324125],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01461866,"threshold_uncertainty_score":0.03630286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07888739278695296,"score_gpt":0.2275123391655099,"score_spread":0.1486249463785569,"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."}}