{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004980518,0.0009887867,0.001250783,0.0002275014,0.0009344954,0.00004383185,0.0004377979,0.0009225624,0.001595699],"category_scores_gemma":[0.0009512404,0.0006527909,0.0005255611,0.0003321589,0.00008070609,0.0001662458,0.000359765,0.001104941,0.00132488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004161682,"about_ca_system_score_gemma":0.00005279032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001671111,"about_ca_topic_score_gemma":0.00006118073,"domain_scores_codex":[0.9953621,0.0003444104,0.0006861667,0.001627752,0.0009284584,0.001051087],"domain_scores_gemma":[0.9957391,0.0009573738,0.0003495153,0.0003548378,0.0005887496,0.002010434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.1548906,0.004215897,0.01498435,0.001112225,0.01254627,0.003911708,0.00003914364,0.00000453702,0.5065339,0.001633289,0.002369933,0.2977582],"study_design_scores_gemma":[0.01059864,0.002725848,0.3947331,0.005693637,0.00527114,0.00000752945,0.0004625896,0.000007202456,0.5006456,0.002685871,0.07422363,0.002945201],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9594691,0.00298232,4.013955e-7,0.0005221426,0.001643224,0.004090926,0.02547521,0.0002724395,0.005544259],"genre_scores_gemma":[0.9831476,0.0005899435,0.0002104403,0.007466148,0.0001721653,0.0005274162,0.006282435,0.00008756045,0.001516331],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3797487,"threshold_uncertainty_score":0.9995924,"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."}}