{"id":"W4387581621","doi":"10.1002/mp.16772","title":"Audit of data from examination image headers collected for quality assurance in the ECOG‐ACRIN EA1151 tomosynthesis mammographic imaging screening trial (TMIST)","year":2023,"lang":"en","type":"article","venue":"Medical Physics","topic":"Digital Radiography and Breast Imaging","field":"Medicine","cited_by":3,"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; Quality assurance; DICOM; Mammography; Image quality; Medical physics; Breast imaging; Imaging phantom; Digital mammography; Computer science; Medical imaging; Data quality; Medicine; Nuclear medicine; Radiology; Artificial intelligence; Breast cancer; Cancer","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":[0.002291639,0.000174693,0.0004595711,0.0001710224,0.00008880474,0.00004884573,0.0005425437,0.00006491349,0.00002780624],"category_scores_gemma":[0.002360204,0.0001371452,0.000190892,0.001803331,0.0003128756,0.0004110181,0.0000954483,0.0002766722,0.000004211724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002098826,"about_ca_system_score_gemma":0.0001558836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003238418,"about_ca_topic_score_gemma":0.00008693973,"domain_scores_codex":[0.9974492,0.0002269998,0.0005492177,0.0004366885,0.0009827709,0.0003551361],"domain_scores_gemma":[0.9962393,0.00273905,0.0001797505,0.0006359196,0.00009037702,0.0001156171],"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":[0.006575089,0.001468232,0.02501436,0.0006925665,0.0003741538,0.0001651749,0.00138871,0.000005285349,0.001813818,0.0002221881,0.01791234,0.9443681],"study_design_scores_gemma":[0.0874651,0.000556606,0.8280814,0.003081568,0.000775495,0.00003827998,0.004705318,0.05097139,0.005920076,0.01389686,0.003432484,0.001075412],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8857136,0.0001856016,0.09838138,0.009847122,0.000352804,0.001859967,0.001096783,0.0002373176,0.002325364],"genre_scores_gemma":[0.9969551,0.00002621901,0.0009649435,0.0003515884,0.0004563624,0.00006845386,0.001131824,0.00002496028,0.00002053304],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9432927,"threshold_uncertainty_score":0.5592617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08528158217417142,"score_gpt":0.3574301673593759,"score_spread":0.2721485851852045,"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."}}