{"id":"W2785530110","doi":"10.1016/j.carj.2017.09.002","title":"Clinical Image Quality and Sensitivity in an Organized Mammography Screening Program","year":2018,"lang":"en","type":"article","venue":"Canadian Association of Radiologists Journal","topic":"Global Cancer Incidence and Screening","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hôpital Saint-François d'Assise; Cancer Care Ontario; Memorial University of Newfoundland; Hôpital du Sacré-Cœur de Montréal; Hôpital Maisonneuve-Rosemont; Université de Montréal; Université Laval; Centre hospitalier universitaire de Québec; Institut National de Santé Publique du Québec","funders":"","keywords":"Medicine; Mammography; Medical physics; Sensitivity (control systems); Quality (philosophy); Screening mammography; Image quality; Mammography screening; Radiology; Artificial intelligence; Image (mathematics); Internal medicine; Breast cancer; Cancer","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005711555,0.0001034367,0.0004399091,0.0002386073,0.0001340115,0.0000525869,0.00005586631,0.0002184803,0.0000451175],"category_scores_gemma":[0.003104293,0.00009714625,0.00009946901,0.0003295716,0.0002474848,0.0002113043,0.00001200277,0.0004798324,0.000002271721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004714351,"about_ca_system_score_gemma":0.0005363858,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01123504,"about_ca_topic_score_gemma":0.174613,"domain_scores_codex":[0.9980061,0.0005713488,0.0006000453,0.0001998897,0.0002500168,0.0003726173],"domain_scores_gemma":[0.9979878,0.0001870487,0.000466542,0.0001289705,0.0006172102,0.0006124095],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001111325,0.00002869728,0.9748603,0.000005736889,0.00004757503,0.0001400539,0.0001132874,5.084485e-7,0.0002580273,0.00004080838,0.001364277,0.02302962],"study_design_scores_gemma":[0.001189615,0.000635021,0.9961973,0.0000712004,0.00003251334,0.0002796881,0.0002385208,0.0001531698,0.0000299004,0.000107851,0.0009682958,0.00009696284],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965172,0.00008631838,0.0003948414,0.001955016,0.0001572506,0.0001852873,0.00001762861,0.00002030645,0.000666197],"genre_scores_gemma":[0.9842119,0.0000719337,0.01427914,0.0009172905,0.0004727278,0.000001538994,0.00001066708,0.000008265215,0.00002652235],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.163378,"threshold_uncertainty_score":0.9953492,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1130444806700274,"score_gpt":0.4347237422169281,"score_spread":0.3216792615469007,"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."}}