{"id":"W4231200303","doi":"10.1002/9780470015902.a0005203.pub2","title":"Insurance and Genetic Information","year":2010,"lang":"en","type":"other","venue":"Encyclopedia of Life Sciences","topic":"BRCA gene mutations in cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Underwriting; Actuarial science; Genetic testing; Pace; Auto insurance risk selection; Business; Population; Genetic discrimination; Key person insurance; Health insurance; Group insurance; Medical underwriting; Insurance policy; Economics; General insurance; Health care; Income protection insurance; Medicine; Environmental health; Economic growth","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00123662,0.0001905328,0.0002050593,0.001623364,0.001160679,0.004442372,0.0004078572,0.002708107,0.03769637],"category_scores_gemma":[0.005699072,0.00009797282,0.000183242,0.002233159,0.003568011,0.003198016,0.002180714,0.001933286,0.003128084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002140251,"about_ca_system_score_gemma":0.001717867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005881403,"about_ca_topic_score_gemma":0.005450797,"domain_scores_codex":[0.9989952,0.0003620369,0.00005016407,0.0001067412,0.0003505788,0.0001352316],"domain_scores_gemma":[0.9968055,0.00194005,0.0004407447,0.0001801765,0.0003149189,0.0003186198],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001941964,0.00002258148,0.002800978,0.0001198357,0.000006894955,0.0003220998,0.0004615834,0.0002584797,0.00009668109,0.8546721,0.07547444,0.06574491],"study_design_scores_gemma":[0.000009348452,0.00001821061,0.005835208,0.0006352042,0.00001408266,0.001365921,0.000706564,0.0004036864,0.0001763288,0.2616124,0.7292067,0.00001632213],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.009432901,0.02639205,0.002319454,0.1134937,0.0009992331,0.00003281478,0.0005347456,0.00005273526,0.8467425],"genre_scores_gemma":[0.6031548,0.05265522,0.003598233,0.03676784,0.003688294,0.00008757364,0.0007121612,0.00005105856,0.2992848],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.03769637,"threshold_uncertainty_score":0.1261069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006423534439720079,"score_gpt":0.2480706403361119,"score_spread":0.2416471058963919,"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."}}