{"id":"W7005054194","doi":"","title":"Profiling biomarker signatures that contribute to conversion from mild cognitive impairment to Alzheimer's disease","year":2016,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Biological and pharmacological studies of plants","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; University of California, San Diego; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Dementia; Disease; Biomarker; Neuroimaging; Population; Logistic regression; Alzheimer's disease; Clinical trial","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":[0.003551038,0.0004079243,0.0005471141,0.001305924,0.0002675675,0.0009667022,0.0002920726,0.0005579053,0.0005533409],"category_scores_gemma":[0.01639333,0.0001781551,0.0005837251,0.0007620556,0.0003030673,0.0004548437,0.0004458452,0.0006481162,0.0001653272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002044108,"about_ca_system_score_gemma":0.0004218989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000988684,"about_ca_topic_score_gemma":0.0015941,"domain_scores_codex":[0.9984913,0.0007807954,0.0001629514,0.0002550062,0.0001947916,0.000115171],"domain_scores_gemma":[0.9913847,0.004823041,0.002384498,0.0007137941,0.0004514315,0.0002425193],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009839457,0.000247641,0.9414843,0.0000655701,0.0003641222,0.000184841,0.0001292267,0.004089716,0.005834546,0.0002158326,0.0004070205,0.04599321],"study_design_scores_gemma":[0.0000549985,0.0005720693,0.9520988,0.00002771122,0.0003000874,0.0006897426,0.0001257617,0.03825893,0.004792583,0.002123252,0.0009146233,0.0000416095],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910353,0.0005548909,0.007204484,0.0001649786,0.00001254538,0.0000520786,0.0004432281,0.00007370867,0.0004588528],"genre_scores_gemma":[0.995564,0.0001040618,0.003690586,0.0000627261,0.00001433239,0.00003007754,0.0004015594,0.000006960672,0.0001257954],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003551038,"threshold_uncertainty_score":0.01877987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04510638125633981,"score_gpt":0.3060592369681714,"score_spread":0.2609528557118316,"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."}}