{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001872129,0.0003020796,0.0003459001,0.0004920674,0.0001582039,0.0003201542,0.0002473645,0.0004623492,0.0006961958],"category_scores_gemma":[0.006464547,0.0002344106,0.0002015799,0.0001615757,0.000284668,0.0003369822,0.0002402048,0.0003042078,0.0001119451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001618657,"about_ca_system_score_gemma":0.0001979049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004886558,"about_ca_topic_score_gemma":0.0006412202,"domain_scores_codex":[0.9990556,0.0005322249,0.00006237094,0.0001215588,0.0001446418,0.00008362312],"domain_scores_gemma":[0.9965916,0.001855729,0.0008852434,0.0001624334,0.000179963,0.0003251514],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00619587,0.001294716,0.9740968,0.00005499789,0.0001308818,0.0001404099,0.000118022,0.0001056579,0.004962414,0.00001173675,0.00007269581,0.01281585],"study_design_scores_gemma":[0.0001688522,0.007410881,0.9891636,0.00001296289,0.0000949212,0.0007140414,0.00007922074,0.0003992904,0.001819152,0.00001462365,0.0001168963,0.000005586841],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999545,0.0002612197,0.00003715773,0.00002231411,0.000002686056,0.000007277924,0.00001176154,0.000002937755,0.0001094475],"genre_scores_gemma":[0.9995821,0.0001079711,0.0001740157,0.00002761939,0.00001497235,0.000006107732,0.0000432273,7.558174e-7,0.00004327742],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001872129,"threshold_uncertainty_score":0.009900868,"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."}}