{"id":"W4404327345","doi":"10.1002/alz.14311","title":"The ADNI Administrative Core: Ensuring ADNI's success and informing future AD clinical trials","year":2024,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":5,"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; National Institutes of Health; Genentech; Takeda Pharmaceutical Company; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; BioClinica; Biogen; GE Healthcare; Novartis Pharmaceuticals Corporation; Eli Lilly and Company; Bristol-Myers Squibb; Merck; Alzheimer's Drug Discovery Foundation; Meso Scale Diagnostics; AbbVie; Fujirebio Europe; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Alzheimer's Disease Neuroimaging Initiative; Neuroimaging; Data sharing; Clinical trial; Core (optical fiber); Imaging biomarker; Biomarker; Cognitive impairment; Computer science; Disease; Medicine; Internal medicine; 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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.6387922,0.002779483,0.006125834,0.01225648,0.01259064,0.04444136,0.01397834,0.01376181,0.03052984],"category_scores_gemma":[0.7505103,0.004949615,0.003582816,0.009098917,0.01332073,0.02953483,0.03693035,0.02105205,0.05245525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01933311,"about_ca_system_score_gemma":0.2555498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01953081,"about_ca_topic_score_gemma":0.02329612,"domain_scores_codex":[0.4629248,0.3437792,0.07143062,0.01707704,0.08748976,0.01729856],"domain_scores_gemma":[0.1404784,0.2062603,0.04682127,0.147416,0.3570136,0.1020104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004126965,0.0002978623,0.006483315,0.001449114,0.0002157542,0.0002247234,0.002346655,0.0006794245,0.0005753139,0.02061224,0.7264627,0.2402403],"study_design_scores_gemma":[0.0007579722,0.0004286302,0.010967,0.006641199,0.0001785369,0.0003425378,0.001355015,0.00183399,0.0008673114,0.02547303,0.9508576,0.0002971539],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.005656981,0.01625632,0.1438798,0.6188509,0.02656121,0.03062915,0.005752845,0.009825082,0.1425877],"genre_scores_gemma":[0.05310525,0.01582983,0.6327803,0.1777584,0.02180444,0.05163199,0.01111889,0.006903925,0.02906701],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6387922,"threshold_uncertainty_score":0.4454336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1720588129421544,"score_gpt":0.4759500623376202,"score_spread":0.3038912493954658,"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."}}