{"id":"W2962851149","doi":"10.3233/jad-190283","title":"Longitudinal Mapping of Cortical Thickness Measurements: An Alzheimer’s Disease Neuroimaging Initiative-Based Evaluation Study","year":2019,"lang":"en","type":"article","venue":"Journal of Alzheimer s Disease","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Mental Health; National Institute on Aging; Canadian Institutes of Health Research","keywords":"Interpretability; Neuroimaging; Statistical power; Context (archaeology); Alzheimer's Disease Neuroimaging Initiative; Computer science; Normalization (sociology); Artificial intelligence; Machine learning; Psychology; Statistics; Cognition; Neuroscience; Mathematics; Cognitive impairment","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":[0.005964412,0.0005432019,0.0003382257,0.0009682655,0.00071414,0.0007654969,0.0004987277,0.0004402616,0.0006311656],"category_scores_gemma":[0.007412699,0.0002167469,0.0004515314,0.0009149354,0.0004870728,0.0006472425,0.0009978347,0.0006384063,0.0003062916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005562203,"about_ca_system_score_gemma":0.0008979949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005745927,"about_ca_topic_score_gemma":0.007480822,"domain_scores_codex":[0.9987657,0.0006818346,0.00008202108,0.0001805398,0.00021101,0.00007887394],"domain_scores_gemma":[0.9965996,0.0005020193,0.0008435905,0.0005386672,0.001031827,0.0004842938],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005941982,0.002515451,0.951938,0.00009435844,0.0006088049,0.000443343,0.0012246,0.001343516,0.003003681,0.0005664496,0.001798108,0.03052183],"study_design_scores_gemma":[0.000180756,0.002460924,0.9896832,0.00002692854,0.0003029325,0.0006162302,0.0004381873,0.00324406,0.00104286,0.000360731,0.001609172,0.00003412251],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965948,0.0001336923,0.001541478,0.00006465938,0.000006526655,0.0001748591,0.0009164365,0.00001789222,0.0005495539],"genre_scores_gemma":[0.9935615,0.0001335395,0.002579863,0.00004290973,0.00001945064,0.0003881595,0.002718757,0.00002365889,0.0005321395],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005964412,"threshold_uncertainty_score":0.0315432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2498752256175815,"score_gpt":0.3804475595093956,"score_spread":0.130572333891814,"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."}}