{"id":"W4309164930","doi":"10.2196/34600","title":"The Use of Machine Learning to Reduce Overtreatment of the Axilla in Breast Cancer: Retrospective Cohort Study","year":2022,"lang":"en","type":"article","venue":"JMIR Perioperative Medicine","topic":"Breast Cancer Treatment Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Breast cancer; Machine learning; Axilla; Retrospective cohort study; Artificial intelligence; Logistic regression; Artificial neural network; Cohort; Cancer; Surgery; Radiology; Internal medicine; Computer science","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":[0.0002105108,0.0001673909,0.0003173019,0.00003475873,0.0003341498,0.000004808489,0.0002237771,0.00001847429,0.00007676851],"category_scores_gemma":[0.0001079039,0.00008759081,0.00005085333,0.0003087998,0.0002262239,0.000003214057,0.0004627871,0.0001494048,2.159434e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002582276,"about_ca_system_score_gemma":0.00008452877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004667205,"about_ca_topic_score_gemma":0.002279303,"domain_scores_codex":[0.9986408,0.0002977116,0.0002589818,0.0003126038,0.000331709,0.0001582236],"domain_scores_gemma":[0.9992256,0.00003504772,0.0001306791,0.0004261979,0.0001515305,0.00003099145],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005714242,0.000281247,0.9652286,0.000003747854,0.0002926844,0.000002831154,0.006110987,0.001093789,0.02508646,0.00001836008,0.0005288388,0.0007810593],"study_design_scores_gemma":[0.001488946,0.001170022,0.9841589,0.00002838727,0.0000464506,0.00001174362,0.00513326,0.00003050601,0.00303981,0.000001116458,0.004808928,0.00008196149],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9923601,0.0009906801,0.000001320128,0.004920579,0.0001422434,0.001323784,0.000141613,0.000003444819,0.0001162074],"genre_scores_gemma":[0.9973851,0.0002780553,0.000009962626,0.0001419714,0.00007026204,0.001213695,0.00001676995,0.00001545599,0.0008687065],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02204665,"threshold_uncertainty_score":0.7055449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01539035219914279,"score_gpt":0.2972146645281628,"score_spread":0.2818243123290199,"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."}}