{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001089415,0.0001611582,0.0002977548,0.00007457769,0.00003709483,0.0000377482,0.0001214236,0.00008470474,0.000009395741],"category_scores_gemma":[0.001485008,0.000156766,0.0002553779,0.0001163112,0.00002901605,0.000007505932,0.00005980735,0.00005819527,5.85155e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001539431,"about_ca_system_score_gemma":0.0008178281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006132426,"about_ca_topic_score_gemma":0.0001159417,"domain_scores_codex":[0.9989328,0.00003994737,0.0004745784,0.0001955584,0.0001515953,0.0002055263],"domain_scores_gemma":[0.9981638,0.0001817234,0.0003318063,0.000261918,0.0008923212,0.0001683911],"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":[0.0002538223,0.0001459949,0.002143394,0.00003397345,0.000268554,0.0001340731,0.00005068417,0.02015597,0.9694302,0.0001257407,0.003105156,0.004152428],"study_design_scores_gemma":[0.001194258,0.001777473,0.0009428074,0.0001447247,0.0003064587,0.00007387099,0.00002784098,0.0001921299,0.9687641,0.00009933453,0.0262975,0.0001795595],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9426275,0.0038361,0.0519128,0.0003846748,0.0003122736,0.0002628024,0.0006457618,0.000002366034,0.00001571807],"genre_scores_gemma":[0.9532164,0.004844028,0.03956547,0.001353808,0.0006927521,0.00003517757,0.0001649492,0.00005863669,0.00006876679],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02319235,"threshold_uncertainty_score":0.6392731,"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."}}