{"id":"W2401611683","doi":"10.1158/2159-8290.cd-nd2012-007","title":"Breast Cancer Screening Goes Personalized","year":2012,"lang":"en","type":"article","venue":"Cancer Discovery","topic":"Nutrition, Genetics, and Disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cancer; Computational biology; Breast cancer; Medicine; Bioinformatics; Computer science; Biology; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005990437,0.0001555457,0.0001217717,0.00002482108,0.00009436243,0.00004503719,0.0001221469,0.00008237478,0.0002662618],"category_scores_gemma":[0.000007618438,0.0001469366,0.0001264719,0.00005871598,0.0001137134,0.00002276568,0.0000652738,0.00004989492,0.000006632574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002995984,"about_ca_system_score_gemma":0.0001239988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001585742,"about_ca_topic_score_gemma":0.00005011361,"domain_scores_codex":[0.9991042,0.00003173276,0.0001226705,0.0002532495,0.0001300966,0.0003580226],"domain_scores_gemma":[0.9994675,0.000004652338,0.00005457619,0.0002236775,0.00006019378,0.0001893945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009408121,0.0003205093,0.6637835,0.0001610288,0.0003116913,0.000002578133,0.0002472899,0.000146048,0.2638017,0.0003500174,0.05645647,0.01347834],"study_design_scores_gemma":[0.004679126,0.0001033851,0.5320244,0.0002529653,0.0003638664,0.00005406471,0.001001748,0.00003987923,0.1591949,0.0001678474,0.3007091,0.001408755],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9482267,0.0476925,0.001387726,0.0004190409,0.0006295803,0.0001320399,0.0008692404,0.00001644388,0.0006267342],"genre_scores_gemma":[0.9885202,0.004366521,0.0001401132,0.0008980552,0.002742802,0.0001223026,0.0001526826,0.00003078039,0.003026553],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2442526,"threshold_uncertainty_score":0.5991899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01468920328017518,"score_gpt":0.2792943442795908,"score_spread":0.2646051409994156,"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."}}