{"id":"W4387597973","doi":"10.1002/mp.16786","title":"Quality control for digital tomosynthesis in the ECOG‐ACRIN EA1151 TMIST trial","year":2023,"lang":"en","type":"article","venue":"Medical Physics","topic":"Digital Radiography and Breast Imaging","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre","funders":"National Cancer Institute; ECOG-ACRIN Cancer Research Group; National Institutes of Health; American College of Radiology Imaging Network","keywords":"Tomosynthesis; Medical physics; Mammography; Digital mammography; Computer science; Image quality; Breast imaging; Quality assurance; Medicine; Breast cancer; Artificial intelligence; Cancer; Pathology","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":[0.0009527761,0.0001386455,0.0003584533,0.00005524071,0.00005888047,0.00007850591,0.0002175061,0.00006877379,0.00002418172],"category_scores_gemma":[0.001768495,0.00008972879,0.0003223291,0.0005474351,0.0001825052,0.0001602168,0.00002558383,0.0002222461,0.00005232241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002173484,"about_ca_system_score_gemma":0.0001120441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001645419,"about_ca_topic_score_gemma":0.000004361599,"domain_scores_codex":[0.9982905,0.00005060323,0.0003401021,0.0002320645,0.0007256031,0.0003611571],"domain_scores_gemma":[0.9978473,0.001631462,0.00005469521,0.0002628797,0.00003884769,0.0001648406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"randomized_trial","study_design_scores_codex":[0.009230327,0.001490141,0.007374706,0.0002185221,0.0001664338,0.0002003071,0.0004910749,0.000002364036,0.00003003594,0.004340053,0.01428116,0.9621749],"study_design_scores_gemma":[0.4748497,0.004687573,0.1070376,0.001817999,0.0008266332,0.000323259,0.003846334,0.008265148,0.001785731,0.2588029,0.1352939,0.002463206],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9062276,0.00007642156,0.02002895,0.03207747,0.001001353,0.002938987,0.0004303404,0.0004309461,0.03678795],"genre_scores_gemma":[0.9969311,0.000004812229,0.00001125252,0.001599504,0.001089731,0.0001296842,0.00008552934,0.0000172888,0.0001310987],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9597117,"threshold_uncertainty_score":0.3659034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0367214464002751,"score_gpt":0.3305829637453457,"score_spread":0.2938615173450706,"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."}}