{"id":"W3091941748","doi":"10.1038/s41467-020-18883-x","title":"Identification of 31 loci for mammographic density phenotypes and their associations with breast cancer risk","year":2020,"lang":"en","type":"article","venue":"Nature Communications","topic":"AI in cancer detection","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute on Aging; Cancer Research UK; Horizon 2020 Framework Programme; European Commission; Ellison Medical Foundation; Canadian Institutes of Health Research; Genome Canada; National Cancer Institute; Kaiser Permanente; Wayne and Gladys Valley Foundation; U.S. Department of Health and Human Services; National Institutes of Health; Government of Canada","keywords":"Breast cancer; Phenotype; MAMMOGRAPHIC DENSITY; Identification (biology); Genome-wide association study; Genetics; Cancer; Mammography; Oncology; Biology; Medicine; Bioinformatics; Single-nucleotide polymorphism; Gene; Genotype","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002135749,0.000091792,0.000132132,0.00006356995,0.0003757066,0.00006807246,0.0008980943,0.0001210838,8.051136e-7],"category_scores_gemma":[0.00008663785,0.00007804356,0.00004644343,0.0006859946,0.0001243693,0.0002854841,0.0002024898,0.0003805667,6.995254e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000537299,"about_ca_system_score_gemma":0.00009292156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009200913,"about_ca_topic_score_gemma":0.0009904007,"domain_scores_codex":[0.9992899,0.00008590648,0.0001944099,0.0002178553,0.0001131598,0.00009880332],"domain_scores_gemma":[0.9979437,0.0002714553,0.0003414052,0.0009149757,0.0004774867,0.00005095323],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002282508,0.0006121971,0.4396705,0.0002646752,0.001387986,3.126596e-7,0.02038369,0.001279709,0.03558339,0.2425613,0.01355569,0.2444723],"study_design_scores_gemma":[0.0009121971,0.0001352411,0.8289439,0.0000418261,0.0001873139,0.000008813954,0.0003324429,0.1425522,0.009436878,0.008920803,0.008100186,0.0004281339],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1298268,0.008630924,0.7928497,0.06544857,0.0002372692,0.001267603,0.001202519,0.0003719754,0.0001645988],"genre_scores_gemma":[0.9836604,0.00073938,0.01515589,0.0002747464,0.00003685084,0.0000945128,0.00002319582,0.000008737108,0.000006294512],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8538336,"threshold_uncertainty_score":0.3182524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01496248834775128,"score_gpt":0.2637428829378849,"score_spread":0.2487803945901337,"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."}}