{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002599175,0.0001423048,0.0003718591,0.0003158524,0.00003582304,0.00002016374,0.0001242162,0.00009333319,0.000003362078],"category_scores_gemma":[0.001284412,0.0001497366,0.0001583733,0.0003805568,0.000180347,0.00001057854,0.0001155445,0.00009342334,1.598579e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001088496,"about_ca_system_score_gemma":0.0004801239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007927047,"about_ca_topic_score_gemma":0.0001518537,"domain_scores_codex":[0.9987379,0.00008451495,0.000619252,0.000181673,0.0001979846,0.0001786837],"domain_scores_gemma":[0.9982516,0.0001906079,0.0006253149,0.0002124298,0.0005843088,0.0001357545],"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":[0.000283863,0.00009213803,0.01487959,0.00002164471,0.001051577,0.0001958432,0.0003098102,0.02776527,0.9549801,0.000113847,0.00003587572,0.0002704092],"study_design_scores_gemma":[0.001525835,0.001228621,0.09326094,0.0002040985,0.005308337,0.0000510173,0.000345186,0.02059093,0.8766717,0.0003103041,0.0001191103,0.0003838926],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9728024,0.00104109,0.02574527,0.00004571376,0.0001079777,0.0000826235,0.0001458566,0.000001003218,0.00002807567],"genre_scores_gemma":[0.9905268,0.001193617,0.007960389,0.0001210553,0.0001488115,7.131486e-7,0.00002815368,0.00001866959,0.000001771958],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07838134,"threshold_uncertainty_score":0.610608,"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."}}