{"id":"W2904004553","doi":"10.1148/radiol.2018181426","title":"Impact of Digital Mammography on Cancer Detection and Recall Rates: 11.3 Million Screening Episodes in the English National Health Service Breast Cancer Screening Program","year":2018,"lang":"en","type":"article","venue":"Radiology","topic":"AI in cancer detection","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Hospital Foundation","funders":"Public Health England; Cancer Research UK","keywords":"Medicine; Mammography; Cancer detection; Breast cancer; Breast cancer screening; Gynecology; Cancer screening; Family medicine; Cancer; Recall; Obstetrics; Medical physics; 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":[0.0005747923,0.000165369,0.0002241218,0.0003013679,0.00017195,0.00008570353,0.0003459373,0.000105467,0.000005821501],"category_scores_gemma":[0.00003891321,0.0001248733,0.00006809725,0.001126174,0.0001583398,0.0004757599,0.00006424006,0.0002155477,5.986519e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002163062,"about_ca_system_score_gemma":0.0001512912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004283741,"about_ca_topic_score_gemma":0.006130867,"domain_scores_codex":[0.9984648,0.0002396638,0.0002851974,0.0004206079,0.0002462788,0.0003434212],"domain_scores_gemma":[0.9989532,0.0001970705,0.0002347623,0.0002133344,0.0003380318,0.00006359749],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002114324,0.00007697538,0.2221618,0.00003296771,0.00007157522,0.000001228025,0.002701388,0.002739753,0.0002149899,0.0001299524,0.0002786086,0.7713793],"study_design_scores_gemma":[0.0006556996,0.001242912,0.928279,0.0001217087,0.000005014234,0.0001130339,0.0001271433,0.06834207,0.0001353419,0.0003010576,0.000494911,0.0001820522],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9484064,0.0004298998,0.04765901,0.00226749,0.0004336079,0.0005377501,0.00006212195,0.0001262493,0.00007750466],"genre_scores_gemma":[0.9964991,0.0001443513,0.002237072,0.0004916789,0.0004600317,0.0001488458,0.000005288413,0.00001175047,0.000001833934],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7711972,"threshold_uncertainty_score":0.6475763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.028080408641976,"score_gpt":0.3347723505787126,"score_spread":0.3066919419367365,"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."}}