{"id":"W1593954915","doi":"10.1002/9780470015902.a0005203.pub3","title":"Insurance and Genetic Information","year":2017,"lang":"en","type":"other","venue":"Encyclopedia of Life Sciences","topic":"BRCA gene mutations in cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"","keywords":"Underwriting; Genetic testing; Actuarial science; Penetrance; Population; Disease; Medical underwriting; Genetic discrimination; Business; Insurability; Insurance policy; Medicine; Genetics; Biology; Insurance law; General insurance; Environmental health; Pathology; Gene","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00009259606,0.0001085715,0.0001225985,0.00009312972,0.00007661148,0.00001856441,0.0003243006,0.0001390233,0.00004634545],"category_scores_gemma":[0.0001982822,0.00009792556,0.00002526145,0.00005016295,0.0005287018,0.000008257915,0.0000929824,0.0000395212,0.00000861946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004478041,"about_ca_system_score_gemma":0.0003740754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000114534,"about_ca_topic_score_gemma":0.0001263741,"domain_scores_codex":[0.9993864,0.00001702699,0.0001379027,0.0001864156,0.0001630414,0.0001092096],"domain_scores_gemma":[0.9993478,0.000006203089,0.0003066439,0.0002647657,0.00002995062,0.00004461618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000006369679,0.000009955878,0.02952415,0.0001127575,0.00003509707,3.920515e-7,0.000102493,0.00003136179,0.0003198253,0.00003648701,0.9304919,0.0393292],"study_design_scores_gemma":[0.0001170385,0.00006279632,0.01306419,0.00004206036,0.000005888694,0.000002494972,0.00002122346,0.000005252992,0.0001934658,0.0000324258,0.9863249,0.0001282352],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.01464346,0.01152872,0.0002390296,0.00008258157,0.0006812522,0.000172379,0.00009235171,0.00001190707,0.9725483],"genre_scores_gemma":[0.1973653,0.1417288,0.037574,0.0008103595,0.003097919,0.0001361768,0.0001626602,0.000223343,0.6189014],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.3536469,"threshold_uncertainty_score":0.3993288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009411417176199078,"score_gpt":0.2653387026347213,"score_spread":0.2559272854585222,"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."}}