{"id":"W1973250008","doi":"10.1158/1078-0432.ccr-07-1270","title":"Magnetic Resonance Imaging of the Breast Improves Detection of Invasive Cancer, Preinvasive Cancer, and Premalignant Lesions during Surveillance of Women at High Risk for Breast Cancer","year":2007,"lang":"en","type":"article","venue":"Clinical Cancer Research","topic":"MRI in cancer diagnosis","field":"Medicine","cited_by":112,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto","funders":"","keywords":"Medicine; Mammography; Breast cancer; Magnetic resonance imaging; Radiology; Cancer; Ductal carcinoma; Ultrasound; Population; Prospective cohort study; Pathology; Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004121708,0.0003204413,0.001128614,0.0002544211,0.000333987,0.00001471897,0.0005099977,0.0002389638,0.0002493433],"category_scores_gemma":[0.001094745,0.0002444809,0.0002873763,0.00105213,0.001906878,0.000125063,0.0005661404,0.0008198738,5.690143e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002044322,"about_ca_system_score_gemma":0.002062612,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.06124215,"about_ca_topic_score_gemma":0.06029425,"domain_scores_codex":[0.994768,0.00045284,0.001516195,0.0009574501,0.001146555,0.001158966],"domain_scores_gemma":[0.9916497,0.003819159,0.0009598191,0.0009006234,0.002228942,0.0004417897],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005850701,0.0002095393,0.7555416,0.002015627,0.000147522,0.000004656837,0.0006370766,0.00006181579,0.09399436,0.00001273454,0.0002757471,0.1412486],"study_design_scores_gemma":[0.003786971,0.0004423017,0.8186448,0.002427686,0.0001232537,0.00002114388,0.0003756701,0.00006922495,0.1734896,0.000135427,0.0002921282,0.0001918497],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9551227,0.03596864,0.00001315418,0.002422896,0.0006299571,0.002343504,0.003455433,0.00002080847,0.00002292646],"genre_scores_gemma":[0.8998401,0.09726612,0.00006538656,0.00007127779,0.0005984605,0.001720125,0.000002842578,0.00007189014,0.0003638578],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1410567,"threshold_uncertainty_score":0.9969643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05557337028759279,"score_gpt":0.4089099272557698,"score_spread":0.353336556968177,"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."}}