{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003571687,0.0002466055,0.0003819143,0.001756907,0.002060137,0.004931672,0.0007063948,0.005017456,0.02665206],"category_scores_gemma":[0.01728253,0.0001854122,0.000358958,0.002125445,0.00817313,0.004349054,0.00333325,0.004456642,0.001818656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003725869,"about_ca_system_score_gemma":0.002947855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006840038,"about_ca_topic_score_gemma":0.003679776,"domain_scores_codex":[0.9968356,0.001396234,0.0001550347,0.0003307271,0.0008862061,0.0003960498],"domain_scores_gemma":[0.9867585,0.008812034,0.001540632,0.0005853527,0.001087305,0.001216148],"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":[0.000038358,0.00003038485,0.00632805,0.0001283466,0.00001989763,0.0004324031,0.0009294643,0.0003278003,0.0000854931,0.8848474,0.0566041,0.05022841],"study_design_scores_gemma":[0.00002550018,0.00004374041,0.00872077,0.001177775,0.00003236487,0.001680663,0.001320613,0.0006999961,0.0001536586,0.61829,0.3678151,0.00003993438],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.02507199,0.0474862,0.005462874,0.527018,0.002446417,0.00006025872,0.0006962194,0.00007504063,0.3916829],"genre_scores_gemma":[0.8324654,0.04214233,0.002935115,0.06647279,0.005106773,0.00009594647,0.0005974529,0.00004382359,0.05014038],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.02665206,"threshold_uncertainty_score":0.08916003,"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."}}