{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001316415,0.0004393319,0.0006351144,0.0008666067,0.00009494169,0.0002962227,0.0003450275,0.0004019624,0.0007305573],"category_scores_gemma":[0.004205959,0.0001465352,0.0003780021,0.0003934081,0.00022024,0.0006298918,0.000399083,0.0003104882,0.0002458971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000146648,"about_ca_system_score_gemma":0.0002508001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001352089,"about_ca_topic_score_gemma":0.002064599,"domain_scores_codex":[0.999604,0.0001374211,0.00002220523,0.0001115869,0.00009103753,0.00003378597],"domain_scores_gemma":[0.9985317,0.0007995066,0.0001677791,0.0001822486,0.0002625725,0.00005611766],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0011355,0.0002695637,0.05813194,0.0002906885,0.0005272099,0.0002518789,0.0001828271,0.1366154,0.08653186,0.002350357,0.002526416,0.7111863],"study_design_scores_gemma":[0.0000197723,0.0002217456,0.02522829,0.000007971592,0.00007362467,0.0002530784,0.00002652754,0.9618829,0.0108132,0.0009785208,0.000473434,0.00002075473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2700429,0.0009134667,0.7275875,0.00008812207,0.0000392404,0.00007104766,0.000196928,0.0006296488,0.000431281],"genre_scores_gemma":[0.7558457,0.0004641796,0.2420956,0.00005294698,0.00005307524,0.00004945293,0.0005097602,0.00006832911,0.0008610514],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001352089,"threshold_uncertainty_score":0.006961942,"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."}}