{"id":"W2894513321","doi":"10.1016/j.jmoldx.2018.08.004","title":"Accurate and Sensitive Analysis of Minimal Residual Disease in Acute Myeloid Leukemia Using Deep Sequencing of Single Nucleotide Variations","year":2018,"lang":"en","type":"article","venue":"Journal of Molecular Diagnostics","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University Health Network","funders":"Sahlgrenska Akademin; Göteborgs Universitet; Sahlgrenska Universitetssjukhuset; Barncancerfonden; Stiftelsen Assar Gabrielssons Fond; Stiftelserna Wilhelm och Martina Lundgrens; American Society for Investigative Pathology","keywords":"Myeloid leukemia; Minimal residual disease; Residual; Biology; Computational biology; Deep sequencing; Genetics; Leukemia; Nucleotide; Cancer research; Gene; Computer science; Genome; Algorithm","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0008150312,0.0004208044,0.0004876393,0.0007322455,0.0001788587,0.0009286139,0.0004797339,0.0006445893,0.0003120203],"category_scores_gemma":[0.001665588,0.0003388555,0.0002702255,0.000274259,0.000302672,0.0003246524,0.0006195962,0.0009384629,0.0001964415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003383263,"about_ca_system_score_gemma":0.0003919097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001033135,"about_ca_topic_score_gemma":0.003257835,"domain_scores_codex":[0.999203,0.0001612168,0.00005449997,0.0002180084,0.0002774909,0.00008564308],"domain_scores_gemma":[0.9994168,0.0002460968,0.00009877919,0.0000744717,0.0001197367,0.00004411372],"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":[0.000850407,0.0001537694,0.1559685,0.000280843,0.0002118671,0.0004046544,0.0002398121,0.009204027,0.6288241,0.001935659,0.003006457,0.19892],"study_design_scores_gemma":[0.0001495668,0.0008680126,0.2521454,0.000157013,0.0005725262,0.004745916,0.000424279,0.2061256,0.5017936,0.01068279,0.02219057,0.000144774],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9125372,0.005079346,0.0761769,0.0007322437,0.0001200821,0.0001197989,0.002715084,0.0005395397,0.001979875],"genre_scores_gemma":[0.9587148,0.0007097345,0.03750363,0.0003838605,0.00005309006,0.00006488027,0.001333519,0.00005660869,0.001179832],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001033135,"threshold_uncertainty_score":0.004310369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01281062327891167,"score_gpt":0.2609577483696416,"score_spread":0.24814712509073,"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."}}