{"id":"W7017007977","doi":"","title":"Alzheimerâs disease early detection from sparse data using brain importance maps","year":2013,"lang":"en","type":"article","venue":"RACO (Revistes Catalanes amb Accés Obert) (Consorci de Serveis Universitaris de Catalunya)","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; Genentech; National Institutes of Health; University of California, Los Angeles; Servier; Eisai; Northern California Institute for Research and Education; University of California, San Diego; BioClinica; Medpace; Biogen; Bristol-Myers Squibb; Eli Lilly and Company; AstraZeneca; Amorfix Life Sciences; Bayer HealthCare; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Synarc; Alzheimer's Association","keywords":"Pattern recognition (psychology); Brain disease; Feature (linguistics); Statistical analysis; Support vector machine; Feature extraction","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":[0.001121664,0.0005220643,0.0006611462,0.002984887,0.0002191585,0.0007022223,0.0004175971,0.0003516313,0.0007758921],"category_scores_gemma":[0.005966493,0.0002639737,0.0005132572,0.001289701,0.0002557334,0.0007629055,0.0009119444,0.0006454502,0.0002244065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002489376,"about_ca_system_score_gemma":0.0003828941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002244189,"about_ca_topic_score_gemma":0.003223371,"domain_scores_codex":[0.9996341,0.0001323469,0.00002315052,0.00005989124,0.0001097694,0.00004073612],"domain_scores_gemma":[0.9976687,0.001619388,0.0001934313,0.0001387712,0.0003120902,0.00006766992],"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":[0.000888319,0.0002100164,0.02270566,0.0003793817,0.0002386828,0.0006531634,0.0003116893,0.1384575,0.0327454,0.006465603,0.005533219,0.7914113],"study_design_scores_gemma":[0.00002863477,0.00007665393,0.01125403,0.00002369068,0.00004532076,0.0004249681,0.00006043866,0.9649907,0.00823959,0.01320534,0.001624831,0.00002583083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1234086,0.0007213412,0.8730199,0.0002998152,0.00004386379,0.00008660507,0.0007130801,0.0009471141,0.0007596904],"genre_scores_gemma":[0.748504,0.0005812132,0.2483316,0.0000614135,0.0001053659,0.0001110828,0.001513658,0.00006892411,0.0007227705],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002984887,"threshold_uncertainty_score":0.005932033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03856767273603847,"score_gpt":0.2869183264490462,"score_spread":0.2483506537130078,"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."}}