{"id":"W4407577764","doi":"10.1186/s12880-025-01580-w","title":"A nomogram for diagnosis of BI-RADS 4 breast nodules based on three-dimensional volume ultrasound","year":2025,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Breast Lesions and Carcinomas","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"BI-RADS; Nomogram; Ultrasound; Volume (thermodynamics); Radiology; Computer science; Medicine; Ultrasonography; Breast imaging; Breast ultrasound; Mammography; Breast cancer; Oncology; Cancer; 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":[0.0004955891,0.0001826612,0.0003970838,0.0002275392,0.00009603325,0.00001557521,0.0001530789,0.0001008504,0.0006584805],"category_scores_gemma":[0.001118217,0.0001441561,0.0002598138,0.0003033239,0.0002669803,0.00003794706,0.00004736666,0.0002154179,0.00001367086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005590009,"about_ca_system_score_gemma":0.0007322078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000147922,"about_ca_topic_score_gemma":0.00006706543,"domain_scores_codex":[0.9982685,0.00004635141,0.0003798753,0.0003584901,0.0006199884,0.0003267544],"domain_scores_gemma":[0.9979407,0.001156001,0.00007368151,0.0003689501,0.0001751679,0.0002855283],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003493279,0.0007686492,0.9191573,0.0004773031,0.00004223072,0.00002883319,0.000009777476,0.00002108572,0.0003037963,0.0005351705,0.01204618,0.0662604],"study_design_scores_gemma":[0.003814711,0.0001091393,0.8738734,0.002347265,0.0002167661,0.00014748,0.00004058291,0.115804,0.0004493217,0.0002273576,0.002803515,0.0001663717],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.860639,0.001321291,0.1112398,0.02255254,0.0007981712,0.001145068,0.0001632475,0.0001864594,0.001954449],"genre_scores_gemma":[0.9903895,0.000005132889,0.005981947,0.002986442,0.0002064885,0.0001435249,0.00006701992,0.00002359762,0.0001963452],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1297505,"threshold_uncertainty_score":0.72099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01235940567699821,"score_gpt":0.2779273160216009,"score_spread":0.2655679103446026,"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."}}