{"id":"W2057592907","doi":"10.1186/bcr1847","title":"Can clinically relevant prognostic subsets of breast cancer patients with four or more involved axillary lymph nodes be identified through immunohistochemical biomarkers? A tissue microarray feasibility study","year":2008,"lang":"en","type":"article","venue":"Breast Cancer Research","topic":"Breast Cancer Treatment Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Advancing Health Outcomes; University of British Columbia; BC Cancer Agency","funders":"Canadian Breast Cancer Research Alliance; Sanofi","keywords":"Breast cancer; Immunohistochemistry; Medicine; Axillary lymph nodes; Internal medicine; Oncology; Biomarker; Tissue microarray; Surgical oncology; Proportional hazards model; Estrogen receptor; Progesterone receptor; Log-rank test; Cytokeratin; Pathology; Cancer; Biology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005836581,0.0005590816,0.000754218,0.0001316573,0.00049834,0.0000416449,0.0007470082,0.0002688244,0.0001821073],"category_scores_gemma":[0.0001932649,0.0004155451,0.0001670421,0.0007686731,0.001668124,0.00003136435,0.0006300088,0.0003533543,0.000005062801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000657124,"about_ca_system_score_gemma":0.001849371,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01776481,"about_ca_topic_score_gemma":0.003836625,"domain_scores_codex":[0.9947992,0.0004876452,0.0008504048,0.001480711,0.001339999,0.001042057],"domain_scores_gemma":[0.9959804,0.0001641327,0.0003310602,0.001200753,0.002052753,0.0002709081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01748344,0.003267109,0.9399843,0.0001732998,0.001505756,0.0001041783,0.001399426,0.00000423072,0.032586,8.505504e-7,0.001329399,0.002162075],"study_design_scores_gemma":[0.007737215,0.0006121726,0.9814049,0.0002567975,0.0001779485,0.0001711646,0.001261858,0.000001660578,0.007815232,0.00000707353,0.00006820115,0.000485747],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910556,0.001654945,0.000007694958,0.00176031,0.0001857389,0.002282539,0.002976801,0.0000353069,0.00004104534],"genre_scores_gemma":[0.9966408,0.000727593,0.0002097175,0.0001134916,0.0002347195,0.001386935,0.0002849635,0.00010879,0.0002929811],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04142069,"threshold_uncertainty_score":0.9998296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06947355535564752,"score_gpt":0.3828976704691213,"score_spread":0.3134241151134737,"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."}}