{"id":"W4389682623","doi":"10.1002/alz.13565","title":"Predicting clinical progression trajectories of early Alzheimer's disease patients","year":2023,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute on Aging; Canadian Institutes of Health Research; Eisai; National Institutes of Health; Alzheimer's Disease Neuroimaging Initiative; U.S. Department of Defense","keywords":"Clinical trial; Cohort; Demographics; Magnetic resonance imaging; Cognition; Medicine; Sample size determination; Alzheimer's Disease Neuroimaging Initiative; Cognitive impairment; Artificial intelligence; Disease; Internal medicine; Psychology; Machine learning; Computer science; Statistics; Radiology; Mathematics; Psychiatry","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":[0.008069099,0.0006809994,0.0005958694,0.0006696762,0.0001914572,0.0008377191,0.0005608019,0.0005117323,0.0009088299],"category_scores_gemma":[0.0171645,0.0002246831,0.0006251301,0.000357213,0.0001841691,0.0004331364,0.0004501847,0.001244584,0.0003305823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005586469,"about_ca_system_score_gemma":0.001349725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005623032,"about_ca_topic_score_gemma":0.005988244,"domain_scores_codex":[0.9991973,0.0004581939,0.0000459694,0.0001544439,0.00008081256,0.00006335227],"domain_scores_gemma":[0.9929112,0.004754315,0.0008064887,0.0004996536,0.0007330736,0.0002952499],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00240472,0.0006603363,0.7193308,0.0001327657,0.000548262,0.0001469728,0.0002034847,0.1904605,0.001329928,0.000658696,0.004677775,0.07944585],"study_design_scores_gemma":[0.0002362186,0.001161687,0.1421253,0.0001080274,0.0004055852,0.0001616786,0.00007033202,0.8491061,0.001964499,0.003340211,0.001278182,0.0000422035],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9671384,0.0006428168,0.02870065,0.0005467385,0.00004328462,0.0001588458,0.001683404,0.0001958801,0.0008900213],"genre_scores_gemma":[0.9907499,0.0001511296,0.007064398,0.00007748065,0.0000154683,0.00008628182,0.001554161,0.00001119666,0.0002899859],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008069099,"threshold_uncertainty_score":0.04267401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05373380030403301,"score_gpt":0.3818612366245638,"score_spread":0.3281274363205308,"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."}}