{"id":"W3123553422","doi":"10.1016/j.jmoldx.2020.12.010","title":"Assessing Limit of Detection in Clinical Sequencing","year":2021,"lang":"en","type":"article","venue":"Journal of Molecular Diagnostics","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; Canada's Michael Smith Genome Sciences Centre","funders":"Genome British Columbia","keywords":"Computational biology; Sensitivity (control systems); DNA sequencing; Limiting; Biology; Deep sequencing; Computer science; Limit (mathematics); Data mining; Genetics; Genome; Mathematics; DNA; Engineering; Gene","routes":{"ca_aff":true,"ca_fund":true,"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":[0.04823411,0.001931673,0.002177114,0.005336251,0.001430447,0.005169965,0.002204551,0.01044264,0.002545526],"category_scores_gemma":[0.1308011,0.002890973,0.001464678,0.00162632,0.003804053,0.00268626,0.002757738,0.003864814,0.001780783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00153977,"about_ca_system_score_gemma":0.001891623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001576589,"about_ca_topic_score_gemma":0.002335168,"domain_scores_codex":[0.9340926,0.03492038,0.003534903,0.009621761,0.01635791,0.001472513],"domain_scores_gemma":[0.8047766,0.1683785,0.007876317,0.006004737,0.0111494,0.001814449],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00533341,0.001095777,0.498942,0.004550534,0.002497759,0.002382716,0.00565393,0.02002985,0.2718734,0.02471762,0.008624861,0.1542981],"study_design_scores_gemma":[0.000255849,0.006397024,0.1580217,0.002661807,0.002462828,0.01507702,0.001632646,0.2028168,0.5206861,0.03296416,0.05650444,0.0005195885],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3168405,0.0578633,0.5866088,0.005819643,0.001749507,0.0007888202,0.002221146,0.004020004,0.02408825],"genre_scores_gemma":[0.7787255,0.003444541,0.1989259,0.01035274,0.000514247,0.0009644826,0.001296742,0.0004544043,0.005321373],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04823411,"threshold_uncertainty_score":0.2550895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02690497950277482,"score_gpt":0.324795931637156,"score_spread":0.2978909521343812,"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."}}