{"id":"W4388837377","doi":"10.2196/46474","title":"Noninvasive Staging of Lymph Node Status in Breast Cancer Using Machine Learning: External Validation and Further Model Development","year":2023,"lang":"en","type":"article","venue":"JMIR Cancer","topic":"Breast Cancer Treatment Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Breast cancer; Lymphovascular invasion; Nomogram; Cohort; Sentinel lymph node; Lymph node; Radiology; Sentinel node; Oncology; Internal medicine; Cancer; Metastasis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006126713,0.0001462354,0.0001540327,0.00007716384,0.00005902664,0.00001025685,0.00005086413,0.00005380169,0.00002359121],"category_scores_gemma":[0.000003987859,0.0001387649,0.0000263215,0.0001363214,0.00004596722,0.000007838559,0.000114369,0.00005227769,0.000001087456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001651033,"about_ca_system_score_gemma":0.0002614753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001555464,"about_ca_topic_score_gemma":0.0003869375,"domain_scores_codex":[0.9991441,0.00002541211,0.0001698274,0.0002760666,0.0001285257,0.0002560986],"domain_scores_gemma":[0.9996887,0.000006723024,0.0001016218,0.00009279863,0.00006674629,0.00004337725],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001307233,0.00002929321,0.8412176,0.00005770208,0.0000889892,0.000003201706,0.001547321,0.04678035,0.1038265,0.000002125277,0.00001630926,0.006299915],"study_design_scores_gemma":[0.002424847,0.00001616855,0.8494055,0.0003183018,0.00004423129,0.00001201914,0.0005079455,0.02518445,0.1213183,0.00002470584,0.0003617715,0.0003818682],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977294,0.001445229,0.0002856303,0.000180122,0.00005288605,0.0001317035,0.0001337778,0.00001271733,0.00002851352],"genre_scores_gemma":[0.9971821,0.001682595,0.0006201286,0.00005912823,0.00006084002,0.0001743919,0.00004693829,0.00002742542,0.0001464062],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0215959,"threshold_uncertainty_score":0.5658666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02159235071639449,"score_gpt":0.3032036995650734,"score_spread":0.2816113488486789,"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."}}