{"id":"W4297266350","doi":"10.1136/jmedgenet-2022-108471","title":"Enhancing the BOADICEA cancer risk prediction model to incorporate new data on <i>RAD51C</i> , <i>RAD51D</i> , <i>BARD1</i> updates to tumour pathology and cancer incidence","year":2022,"lang":"en","type":"article","venue":"Journal of Medical Genetics","topic":"BRCA gene mutations in cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Centre hospitalier de l'Université Laval; Centre hospitalier universitaire de Québec","funders":"NIHR Cambridge Biomedical Research Centre; European Commission; Cancer Research UK; Government of Canada; Fondation du cancer du sein du Québec; Canadian Institutes of Health Research; National Institute for Health and Care Research; Genome Canada","keywords":"PALB2; Breast cancer; CHEK2; Oncology; Cancer; Medicine; Population; Internal medicine; Family history; Incidence (geometry); Biology; Environmental health; Genetics; Gene; Mutation; Germline mutation","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":[0.00189804,0.0002140955,0.0002785451,0.0001005276,0.0003075555,0.00004468103,0.001368013,0.0001424063,0.00009256848],"category_scores_gemma":[0.0005495416,0.0001703323,0.00006019559,0.0003054019,0.000105722,0.00001169194,0.00139705,0.000699005,0.000002825087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001720064,"about_ca_system_score_gemma":0.002544108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001567037,"about_ca_topic_score_gemma":0.0007950821,"domain_scores_codex":[0.9970822,0.0002615241,0.0006845018,0.0004909207,0.001151128,0.0003297532],"domain_scores_gemma":[0.9981082,0.00007497824,0.000460172,0.0006426382,0.0001877406,0.0005262636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005657825,0.0001214336,0.004742901,0.00002405264,0.0002302565,0.00008344893,0.001145539,0.3967263,0.1981295,0.00002085568,0.3304828,0.06772719],"study_design_scores_gemma":[0.004006955,0.004351096,0.005508967,0.0003656285,0.0008247939,0.001693523,0.001335417,0.05138437,0.2023842,0.001373968,0.7256311,0.001140007],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9301774,0.01163865,0.0326319,0.02281653,0.001613492,0.0003449683,0.000733773,0.000009467934,0.00003384404],"genre_scores_gemma":[0.9465085,0.01993463,0.007583009,0.02305359,0.002464698,0.00009157823,0.00005645559,0.0000647577,0.0002428446],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3951482,"threshold_uncertainty_score":0.6945948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02380207630060988,"score_gpt":0.3126643860781211,"score_spread":0.2888623097775112,"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."}}