{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002917821,0.0008429238,0.0008768167,0.001155248,0.0004813612,0.00109176,0.00158897,0.000756952,0.004966278],"category_scores_gemma":[0.006877587,0.0004365745,0.001620495,0.0007899954,0.0003028651,0.0006576377,0.001147438,0.001513655,0.0007979506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001128054,"about_ca_system_score_gemma":0.001908327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06096458,"about_ca_topic_score_gemma":0.05124542,"domain_scores_codex":[0.9992509,0.0003468853,0.0000373224,0.000214801,0.00007313088,0.00007687379],"domain_scores_gemma":[0.9974389,0.001850153,0.0001138869,0.0001337746,0.0003603,0.0001029936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003547829,0.0001643731,0.04829357,0.00008060312,0.0004678899,0.0001770777,0.0001329198,0.8532553,0.0005915884,0.005177562,0.003654362,0.08765001],"study_design_scores_gemma":[0.00002697383,0.0000409789,0.00264304,0.0000175314,0.00006506936,0.00004598908,0.00001423316,0.9929865,0.0001132754,0.002313558,0.001719598,0.00001324928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2998992,0.001225146,0.6850899,0.001910259,0.0002960188,0.0002460024,0.004179433,0.002635665,0.004518409],"genre_scores_gemma":[0.8605053,0.0004214345,0.1276608,0.0003524415,0.000151753,0.0003983754,0.003587381,0.0001747703,0.006747821],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06096458,"threshold_uncertainty_score":0.1212195,"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."}}