{"id":"W2154123014","doi":"10.1016/j.nicl.2012.10.002","title":"Scoring by nonlocal image patch estimator for early detection of Alzheimer's disease","year":2012,"lang":"en","type":"article","venue":"NeuroImage Clinical","topic":"Alzheimer's disease research and treatments","field":"Medicine","cited_by":114,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"Canadian Institutes of Health Research; University of California, San Diego; Genentech; University of California, Los Angeles; U.S. Food and Drug Administration; National Institutes of Health; Eisai; Ministerio de Ciencia e Innovación; Northern California Institute for Research and Education; Alzheimer's Disease Neuroimaging Initiative; F. Hoffmann-La Roche; Medpace; GlaxoSmithKline; AstraZeneca; Eli Lilly and Company; Bristol-Myers Squibb; Elan; Novartis; Synarc; Dana Foundation; Pfizer; National Institute on Aging; Alzheimer's Association","keywords":"Entorhinal cortex; Cognitive impairment; Estimator; Grading (engineering); Alzheimer's disease; Artificial intelligence; Pattern recognition (psychology); Computer science; Pathology; Neuroscience; Disease; Medicine; Psychology; Biology; Hippocampus; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004627527,0.000218455,0.0004680768,0.00007130446,0.000107492,0.00002303696,0.0001039515,0.0001018224,0.00005272562],"category_scores_gemma":[0.001044777,0.0001888259,0.0004401888,0.0001340396,0.0002968009,0.0003113761,0.00008060279,0.0003024227,0.0001285403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002384815,"about_ca_system_score_gemma":0.0001579264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003550533,"about_ca_topic_score_gemma":6.807316e-7,"domain_scores_codex":[0.9977666,0.0001409008,0.0006269694,0.0004351559,0.0004340936,0.0005962962],"domain_scores_gemma":[0.9972667,0.0004589673,0.0001544784,0.0005115016,0.0001917032,0.001416716],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005563492,0.005986845,0.9322118,0.0002219306,0.003019936,0.0002054142,0.0000275887,2.859956e-7,0.006679724,0.0000225763,0.00323789,0.04282251],"study_design_scores_gemma":[0.006277324,0.002019254,0.9676673,0.00009411935,0.002878948,0.00002069928,0.00001547752,0.002286925,0.01763661,0.0000533616,0.0007840058,0.0002659978],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9902132,0.00372707,0.003547118,0.0003590155,0.0004078686,0.001232458,0.0002316704,0.0001008036,0.0001807919],"genre_scores_gemma":[0.9974402,0.00008960524,0.001582233,0.0001517475,0.0004682081,0.0001131083,0.00006950399,0.00006267076,0.00002271403],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04255651,"threshold_uncertainty_score":0.7700095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08276167632788829,"score_gpt":0.4133085518934214,"score_spread":0.3305468755655331,"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."}}