{"id":"W4287702531","doi":"10.3390/cancers15041090","title":"Combining Breast Cancer Risk Prediction Models","year":2023,"lang":"en","type":"article","venue":"Cancers","topic":"BRCA gene mutations in cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. National Library of Medicine; National Science Foundation; National Institutes of Health; National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; American Cancer Society","keywords":"Penetrance; Breast cancer; Family history; Medicine; Computer science; Cancer; Oncology; Internal medicine; Genetics; Biology; Phenotype; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00469469,0.002019862,0.002125401,0.002202779,0.0004232036,0.001705526,0.00176757,0.001128569,0.002110447],"category_scores_gemma":[0.008462119,0.0007586675,0.002064721,0.001562227,0.0002720596,0.001746734,0.001628128,0.002137162,0.001218613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008872899,"about_ca_system_score_gemma":0.0009233631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0124435,"about_ca_topic_score_gemma":0.01099275,"domain_scores_codex":[0.9979244,0.0007671615,0.0001269664,0.000562675,0.0004564518,0.0001622763],"domain_scores_gemma":[0.9960912,0.002556811,0.0002710151,0.0003199915,0.000612663,0.0001483383],"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":[0.0002746469,0.0001996342,0.03440002,0.00008865778,0.000794085,0.0002356988,0.00006696014,0.7961633,0.0008065762,0.001683387,0.004515624,0.1607715],"study_design_scores_gemma":[0.00001262507,0.0000709663,0.002876544,0.00001742404,0.0001465571,0.00008576707,0.00001587026,0.9911522,0.000249376,0.004224908,0.001118504,0.00002923961],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.212907,0.004725425,0.7605094,0.003392914,0.0004361799,0.0004292039,0.004330409,0.003539609,0.009729942],"genre_scores_gemma":[0.8768882,0.002123843,0.1093602,0.0005792262,0.0004603326,0.0003265528,0.004507039,0.0001762839,0.005578288],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0124435,"threshold_uncertainty_score":0.0248282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01658148580237159,"score_gpt":0.2735899476425392,"score_spread":0.2570084618401677,"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."}}