{"id":"W2778967530","doi":"10.1002/hbm.23922","title":"Factors influencing accuracy of cortical thickness in the diagnosis of Alzheimer's disease","year":2017,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Center for Research Resources; National Institute of Neurological Disorders and Stroke; Canadian Institutes of Health Research; National Institute of Nursing Research; University Grants Commission; National Institute on Aging; National Institutes of Health; Alzheimer's Disease Neuroimaging Initiative","keywords":"Neuroimaging; Biomarker; Neuropsychology; Cognition; Alzheimer's Disease Neuroimaging Initiative; Medicine; Disease; Hyperintensity; Confounding; Magnetic resonance imaging; Alzheimer's disease; Gold standard (test); Dementia; Neuroscience; Psychology; Pathology; Radiology; 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":[0.01231919,0.00044942,0.0004290213,0.001230681,0.0004878083,0.001333353,0.000629427,0.0007662837,0.001171995],"category_scores_gemma":[0.09221691,0.000244095,0.0003868183,0.0008687352,0.0008693305,0.0006840358,0.000490536,0.0006799086,0.0005202605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003869772,"about_ca_system_score_gemma":0.0003006677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00434212,"about_ca_topic_score_gemma":0.005506336,"domain_scores_codex":[0.9910618,0.004445836,0.001149727,0.001514977,0.001470886,0.0003567107],"domain_scores_gemma":[0.9167926,0.05894386,0.01271227,0.00519913,0.005181711,0.001170485],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003153006,0.00001897841,0.9931788,0.00001148437,0.0001326129,0.00006558878,0.0001665355,0.0005760705,0.0003896011,0.00005472523,0.0001924877,0.004897671],"study_design_scores_gemma":[0.000006753466,0.0001138235,0.9925397,0.00002305432,0.00006921771,0.0005012924,0.0001631582,0.005057148,0.0008400533,0.0004108375,0.0002625604,0.00001240883],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961736,0.0006846341,0.001355483,0.0002743387,0.00004492101,0.00001685198,0.0001868149,0.00002332354,0.001240143],"genre_scores_gemma":[0.9992138,0.0000565166,0.0004746228,0.00002929675,0.00001727098,0.000004737568,0.00008352469,0.000007892074,0.000112321],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01231919,"threshold_uncertainty_score":0.06515092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1188508098528682,"score_gpt":0.3906374682870652,"score_spread":0.271786658434197,"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."}}