{"id":"W2592274899","doi":"10.1158/1538-7445.sabcs16-p3-02-06","title":"Abstract P3-02-06: Magnetic resonance imaging (MRI) surveillance for patients with dense breasts and a previous breast cancer (BC) and/or high risk lesion","year":2017,"lang":"en","type":"article","venue":"Cancer Research","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; McMaster University; University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Medicine; Breast cancer; Mammography; Breast MRI; Magnetic resonance imaging; Radiology; Stage (stratigraphy); Cancer; Retrospective cohort study; Breast imaging; 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":[0.0004925592,0.0001011398,0.0001719017,0.0007110236,0.000260615,0.0002931542,0.0002549913,0.0001813142,0.002618633],"category_scores_gemma":[0.002226073,0.0001078774,0.0001779484,0.0008325577,0.0001646501,0.0002186638,0.0002276323,0.0001760968,0.0003120093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004517503,"about_ca_system_score_gemma":0.0006285952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004742894,"about_ca_topic_score_gemma":0.007592499,"domain_scores_codex":[0.999775,0.00005967833,0.00004114556,0.00003777811,0.00004530033,0.00004118384],"domain_scores_gemma":[0.9982708,0.0003728299,0.000852356,0.00005465607,0.0001530974,0.0002964262],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001890442,0.00003617253,0.9960069,0.00001707996,0.000007570888,0.0001273521,0.00002279229,0.00003272173,0.0002068647,0.000007277436,0.000324724,0.003021513],"study_design_scores_gemma":[0.00001588381,0.0002491391,0.9982742,0.00001326896,0.00001368746,0.0006201073,0.00006901375,0.0001633144,0.0002422409,0.000007869102,0.0003290792,0.000002164408],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966034,0.000223994,0.0001457319,0.00008077514,0.000007465761,0.00007774955,0.001114876,0.00001224041,0.001733687],"genre_scores_gemma":[0.998669,0.0001005953,0.0002807677,0.00003315796,0.00002001975,0.0000310725,0.0006730226,0.000002148258,0.0001901353],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004742894,"threshold_uncertainty_score":0.009430587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02262921177366353,"score_gpt":0.3667564128170577,"score_spread":0.3441272010433941,"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."}}