{"id":"W2185234758","doi":"","title":"Estimating relative survival for cancer: An analysis of bias introduced by outdated life tables.","year":2014,"lang":"en","type":"article","venue":"PubMed","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Relative survival; Demography; Cancer registry; Population; Survival analysis; Cancer; Medicine; Statistics; Internal medicine; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.004639228,0.0001511081,0.0005072695,0.0003153064,0.0003551138,0.00008445792,0.000387011,0.00008975498,0.00003901014],"category_scores_gemma":[0.002250829,0.0001498705,0.0001982886,0.001720116,0.0002557452,0.0003694025,0.00004538897,0.00009256009,0.000001123043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001082572,"about_ca_system_score_gemma":0.00005212807,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02061955,"about_ca_topic_score_gemma":0.02228059,"domain_scores_codex":[0.9974393,0.0005586255,0.0004561833,0.0004295498,0.0005495497,0.0005667865],"domain_scores_gemma":[0.9983435,0.0003621093,0.0004375024,0.0003456223,0.0002991395,0.0002121133],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007991956,0.000346244,0.8085122,0.00007693882,0.003112704,3.470886e-7,0.006055444,0.01069167,0.00002205046,0.02435607,0.003794205,0.1429522],"study_design_scores_gemma":[0.0007547699,0.00003602493,0.9347942,0.000008465995,0.001781382,9.839539e-9,0.001661789,0.03889865,0.00010635,0.001490171,0.02005056,0.0004175643],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9706883,0.0001652256,0.01789008,0.001360143,0.001256377,0.001856139,0.0003098531,0.0001605911,0.006313259],"genre_scores_gemma":[0.9967332,0.00003312526,0.001182374,0.0001312161,0.000320759,0.001080942,0.0001209178,0.00001751596,0.0003799556],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1425346,"threshold_uncertainty_score":0.9955602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05181850995508321,"score_gpt":0.315722826682142,"score_spread":0.2639043167270588,"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."}}