{"id":"W4399298302","doi":"10.3390/cancers16112116","title":"Implementing Multifactorial Risk Assessment with Polygenic Risk Scores for Personalized Breast Cancer Screening in the Population Setting: Challenges and Opportunities","year":2024,"lang":"en","type":"article","venue":"Cancers","topic":"Global Cancer Incidence and Screening","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; McGill University; Université Laval; Ministère de la Santé et des Services Sociaux (Québec); Sunnybrook Health Science Centre; Women's College Hospital; Cancer Care Ontario; Public Health Ontario; University Health Network; University of Toronto","funders":"Centre Hospitalier Universitaire de Québec; Cancer Research UK; Génome Québec; Canadian Institutes of Health Research; Genome Canada; Fondation du cancer du sein du Québec; Université Laval","keywords":"Polygenic risk score; Breast cancer; Medicine; Risk assessment; Population; Oncology; Cancer; Internal medicine; Computer science; Environmental health; Biology; Genetics; Genotype; Single-nucleotide polymorphism; Gene","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009225731,0.0001783332,0.0002344665,0.0001047097,0.0002441526,0.0001001324,0.00007512363,0.00005363904,0.00004504373],"category_scores_gemma":[0.00003460214,0.0001186682,0.00007312482,0.0001034944,0.0000717732,0.0002276828,0.0000271759,0.000269242,1.521689e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000508698,"about_ca_system_score_gemma":0.0004841906,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01115303,"about_ca_topic_score_gemma":0.006106897,"domain_scores_codex":[0.9985877,0.0000927031,0.000231078,0.0003399529,0.0003532732,0.0003953006],"domain_scores_gemma":[0.9993794,0.0002035819,0.0001298471,0.0001317645,0.00007714733,0.0000782737],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001003257,0.000007326826,0.4612735,0.0003802651,0.0003566033,0.00003949395,0.007888981,0.000465623,0.0000629364,0.001850219,0.0003752209,0.5262966],"study_design_scores_gemma":[0.00368712,0.0003708589,0.9249346,0.00186806,0.000705297,0.00009407359,0.03091923,0.0216209,0.00002225348,0.0003016692,0.01511281,0.0003630959],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9717555,0.02194255,0.00104452,0.003364129,0.000319088,0.0008644664,0.0003886027,0.00006947884,0.0002516773],"genre_scores_gemma":[0.9891006,0.008296554,0.00123365,0.0002087861,0.0007726376,0.0002609936,0.00005196369,0.00002611698,0.00004865981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5259334,"threshold_uncertainty_score":0.9954318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1232520973277731,"score_gpt":0.3867762780226655,"score_spread":0.2635241806948924,"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."}}