{"id":"W3176757391","doi":"10.1016/j.jmoldx.2021.06.006","title":"Use of Treatment-Focused Tumor Sequencing to Screen for Germline Cancer Predisposition","year":2021,"lang":"en","type":"article","venue":"Journal of Molecular Diagnostics","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Spinal Cord Injury BC; University of British Columbia; BC Cancer Agency; Canada's Michael Smith Genome Sciences Centre","funders":"Natural Sciences and Engineering Research Council of Canada; BC Cancer Foundation; Genome British Columbia; Canadian Institutes of Health Research; AstraZeneca Canada; Canada's Michael Smith Genome Sciences Centre; California HIV/AIDS Research Program; AstraZeneca","keywords":"Germline; Cancer; Biology; Germline mutation; Somatic cell; Genetics; Computational biology; Genetic testing; Deep sequencing; Cancer research; Gene; Mutation; Genome","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":[0.000739972,0.0006074356,0.0004643633,0.00159503,0.0003350015,0.0005377085,0.0004437615,0.00104688,0.0014064],"category_scores_gemma":[0.002048771,0.0003673468,0.0005139334,0.0005358058,0.0002575377,0.0002353808,0.0004382682,0.0007587778,0.0004207521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002791952,"about_ca_system_score_gemma":0.0004017636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00186922,"about_ca_topic_score_gemma":0.005123292,"domain_scores_codex":[0.9992755,0.0001718548,0.00003179398,0.0002183238,0.0002347566,0.00006776075],"domain_scores_gemma":[0.9991407,0.0004467515,0.00008669225,0.00009291837,0.0001496175,0.00008326568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0007634067,0.0003619874,0.1469266,0.000195711,0.0004631539,0.002101832,0.0003222934,0.004029302,0.6828907,0.001292824,0.003457023,0.1571952],"study_design_scores_gemma":[0.0002534293,0.002075481,0.2188562,0.0001448041,0.001053067,0.02162728,0.0003275518,0.07660317,0.64612,0.005917998,0.0269293,0.00009179571],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8423059,0.005997701,0.1262106,0.002137881,0.000329243,0.0004440217,0.004515443,0.00211548,0.01594384],"genre_scores_gemma":[0.9322953,0.001274034,0.0599119,0.001824615,0.00006472364,0.0001491265,0.001529818,0.0001534476,0.00279703],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00186922,"threshold_uncertainty_score":0.004704893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02694577494390545,"score_gpt":0.2833067702764059,"score_spread":0.2563609953325005,"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."}}