{"id":"W3120348542","doi":"10.1200/cci.20.00078","title":"Machine Learning Frameworks to Predict Neoadjuvant Chemotherapy Response in Breast Cancer Using Clinical and Pathological Features","year":2021,"lang":"en","type":"article","venue":"JCO Clinical Cancer Informatics","topic":"Breast Cancer Treatment Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre; York University; University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Receiver operating characteristic; Breast cancer; Medicine; Oncology; Logistic regression; Internal medicine; Pathological; Nomogram; Naive Bayes classifier; Machine learning; Artificial intelligence; Cancer; Support vector machine; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001117917,0.0002940095,0.0006142087,0.00005019739,0.0001191467,0.00005648225,0.0001780855,0.0007164752,0.00006442142],"category_scores_gemma":[0.0008261152,0.0002424926,0.0001834694,0.0002313325,0.000272402,0.00001588717,0.00038786,0.001029289,0.000003351092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006835159,"about_ca_system_score_gemma":0.0003657913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001231083,"about_ca_topic_score_gemma":0.0002414923,"domain_scores_codex":[0.9973962,0.0003913979,0.001145293,0.0004197539,0.0002328653,0.0004144365],"domain_scores_gemma":[0.9986272,0.0003198046,0.0002543062,0.0003623334,0.0001891909,0.0002471917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004400545,0.0001985159,0.9335836,0.00003550022,0.0001489598,0.00003131851,0.000421721,0.001573507,0.0002082713,0.00000820241,0.001050264,0.05833966],"study_design_scores_gemma":[0.003137171,0.0003804465,0.978191,0.0002201906,0.00007157137,0.0001002223,0.0003735698,0.002047061,0.0002338543,0.0000344795,0.01484849,0.0003618985],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895459,0.003763835,0.0006738596,0.004900561,0.0005234378,0.0002491751,0.000235485,0.00002845382,0.00007931134],"genre_scores_gemma":[0.9644282,0.01549385,0.006665544,0.01228775,0.0008059277,0.00008519785,0.00005260854,0.00004040824,0.0001404747],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05797776,"threshold_uncertainty_score":0.9888563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03280484919496977,"score_gpt":0.3903093019954161,"score_spread":0.3575044528004463,"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."}}