{"id":"W6992868091","doi":"","title":"Mena[superscript calc], a quantitative method of metastasis assessment, as a prognostic marker for axillary node-negative breast cancer","year":2015,"lang":"en","type":"other","venue":"DSpace@MIT (Massachusetts Institute of Technology)","topic":"Breast Cancer Treatment Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; Canadian Institutes of Health Research","keywords":"Breast cancer; Proportional hazards model; Hazard ratio; Cohort; Biomarker; Survival analysis; Chemotherapy; Cancer; Univariate analysis","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003334434,0.0009029746,0.00154012,0.0008367463,0.0001127352,0.00001533268,0.0007133877,0.001177838,0.0001261575],"category_scores_gemma":[0.0005516817,0.0008079142,0.0003976905,0.0007420035,0.001221681,0.00002104054,0.0004960899,0.0003515889,0.000006401518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002147177,"about_ca_system_score_gemma":0.00112369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001508793,"about_ca_topic_score_gemma":0.002000049,"domain_scores_codex":[0.9969543,0.0001231123,0.0006309701,0.001181917,0.0004645872,0.0006451465],"domain_scores_gemma":[0.996889,0.0000594819,0.001060279,0.001050262,0.0008065687,0.0001343875],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001859589,0.001724113,0.02372932,0.002209679,0.03446256,0.00007868982,0.0005546926,0.00008699513,0.02491433,0.005492345,0.8823554,0.02253228],"study_design_scores_gemma":[0.00520794,0.001113345,0.00156223,0.001318108,0.003187012,0.0001350363,0.001277307,0.00004730023,0.0191038,0.0007690698,0.9648734,0.001405457],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.02017863,0.1408729,0.09247358,0.0450861,0.01390743,0.03188216,0.2684057,0.002573538,0.3846199],"genre_scores_gemma":[0.05207708,0.006556478,0.8330699,0.0005494904,0.0006480329,0.006580784,0.002920671,0.001918957,0.09567858],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7405964,"threshold_uncertainty_score":0.9994372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02092830043189363,"score_gpt":0.3394788475247413,"score_spread":0.3185505470928476,"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."}}