{"id":"W1995362795","doi":"10.1016/j.jalz.2013.11.009","title":"Extension and refinement of the predictive value of different classes of markers in ADNI: Four‐year follow‐up data","year":2014,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; Novartis Pharmaceuticals Corporation; Servier; GE Healthcare; BioClinica; National Institute on Aging; Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Merck; Takeda Pharmaceutical Company; Eli Lilly and Company; Bristol-Myers Squibb","keywords":"Positron emission tomography; Neuroimaging; Logistic regression; Alzheimer's Disease Neuroimaging Initiative; Magnetic resonance imaging; Internal medicine; Cognition; Oncology; Medicine; Psychology; Standardized uptake value; Alzheimer's disease; Disease; Nuclear medicine; Radiology; Neuroscience","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":[0.02232954,0.0008203795,0.0009700348,0.001935613,0.0008758044,0.001591886,0.001926782,0.0009264643,0.001033584],"category_scores_gemma":[0.04544714,0.00040862,0.00164073,0.001864151,0.0005558361,0.001505047,0.001389058,0.002130763,0.0004353203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009913018,"about_ca_system_score_gemma":0.001257673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02022614,"about_ca_topic_score_gemma":0.01757148,"domain_scores_codex":[0.9957137,0.002331222,0.0003845635,0.0007259977,0.0005570814,0.0002874172],"domain_scores_gemma":[0.9738981,0.01160616,0.00469036,0.005966704,0.002900312,0.0009383408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000992002,0.00008774631,0.9916012,0.00002746663,0.0004936071,0.00006036925,0.0001244164,0.001232868,0.0001079745,0.0000586746,0.0003469257,0.004866718],"study_design_scores_gemma":[0.00008668696,0.0004021425,0.988815,0.00004473818,0.0005772196,0.0002649991,0.0001193203,0.00808773,0.0002878489,0.0003863875,0.0009016803,0.00002639571],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9940183,0.0005065331,0.001792607,0.0001816075,0.00002782633,0.00007132785,0.002772711,0.00005228769,0.0005768706],"genre_scores_gemma":[0.9948367,0.00009097454,0.001058346,0.00002884155,0.00001601066,0.00006916893,0.003745126,0.00001082121,0.0001440178],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02232954,"threshold_uncertainty_score":0.1180913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04143194412082846,"score_gpt":0.3046991378938908,"score_spread":0.2632671937730623,"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."}}