{"id":"W4412196595","doi":"10.1016/j.clinimag.2025.110541","title":"Corrigendum to “Diversity, inclusivity and traceability of mammography datasets used in development of Artificial Intelligence technologies: a systematic review” [Clin Imaging 118 (2025) 110369]","year":2025,"lang":"en","type":"erratum","venue":"Clinical Imaging","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"SickKids Foundation","funders":"","keywords":"Traceability; Medicine; Mammography; Diversity (politics); Medical physics; Software engineering; Internal medicine; Engineering; Anthropology","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":["metaresearch","metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.008112692,0.0005187276,0.003416577,0.0008264749,0.000173306,0.00003832985,0.0009219186,0.0003134751,0.00002359501],"category_scores_gemma":[0.02100984,0.000464136,0.0004042739,0.001261917,0.000935265,0.0001147706,0.003133199,0.00273494,0.000005145113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001861648,"about_ca_system_score_gemma":0.0008196683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000169854,"about_ca_topic_score_gemma":0.00005838423,"domain_scores_codex":[0.993011,0.0005137094,0.003955997,0.00116454,0.0008138198,0.0005409375],"domain_scores_gemma":[0.9957983,0.001071276,0.001271136,0.001281215,0.0003181729,0.0002598547],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0001910951,0.001513658,0.2411098,0.4957387,0.0004527296,0.0003359807,0.001004069,0.000002930585,0.0000766852,0.000079903,0.1266544,0.13284],"study_design_scores_gemma":[0.001673079,0.0003004713,0.0242217,0.9146115,0.004544837,0.00009056136,0.003936551,0.03281451,0.0003250356,0.002273345,0.01326439,0.001943982],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.08501428,0.4007774,0.2483494,0.09833356,0.09590956,0.05766257,0.00297516,0.003395225,0.0075829],"genre_scores_gemma":[0.7163932,0.05507797,0.1936489,0.01981774,0.001464042,0.0008621718,0.005553897,0.0005761756,0.006605931],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6313789,"threshold_uncertainty_score":0.999781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05418804972057811,"score_gpt":0.3956368612046027,"score_spread":0.3414488114840246,"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."}}