{"id":"W2759978833","doi":"10.9778/cmajo.20170030","title":"Breast cancer survival by molecular subtype: a population-based analysis of cancer registry data","year":2017,"lang":"en","type":"article","venue":"CMAJ Open","topic":"Breast Cancer Treatment Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":408,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cancer Care Ontario","funders":"Cancer Care Ontario","keywords":"Medicine; Hazard ratio; Breast cancer; Oncology; Internal medicine; Proportional hazards model; Cancer; Comorbidity; Estrogen receptor; Cancer registry; Confidence interval; Population; Stage (stratigraphy); Progesterone receptor; Gynecology; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001275162,0.0001676654,0.000350501,0.00003403651,0.0002000981,0.00009213194,0.001527334,0.00008827338,0.0001586903],"category_scores_gemma":[0.00004224215,0.0001542418,0.00008886064,0.0001156194,0.0001031124,0.00001550402,0.00088767,0.00004509641,0.000001410815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004199856,"about_ca_system_score_gemma":0.000195099,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1633764,"about_ca_topic_score_gemma":0.04382299,"domain_scores_codex":[0.9988852,0.00004049937,0.0001892533,0.0005371529,0.0001613827,0.0001865426],"domain_scores_gemma":[0.9973409,0.000009662794,0.0003462573,0.002120603,0.0001204272,0.00006216896],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001730064,0.00007175878,0.9814909,0.00001598897,0.002255228,0.000002415876,0.000008591306,0.0002688136,0.007933399,0.00001570587,0.003761222,0.004003],"study_design_scores_gemma":[0.0008186222,0.00001225219,0.9867836,0.0000279963,0.001129956,6.464662e-7,0.00001642651,0.0002197936,0.008196863,0.000006213029,0.002584905,0.0002027869],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.981702,0.002395473,0.0002619107,0.002415132,0.0002246296,0.0003350277,0.01168002,0.000008306322,0.0009775484],"genre_scores_gemma":[0.9959229,0.0002034771,0.0001369931,0.0001477447,0.00007311103,0.00008629893,0.003072463,0.00002153539,0.0003354546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1195534,"threshold_uncertainty_score":0.9736248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03933172059223837,"score_gpt":0.3662867462070122,"score_spread":0.3269550256147739,"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."}}