{"id":"W2119396122","doi":"10.1002/sim.1484","title":"A relative survival regression model using B‐spline functions to model non‐proportional hazards","year":2003,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":106,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal General Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Ligue Contre le Cancer","keywords":"Proportional hazards model; Covariate; Relative survival; Statistics; Regression analysis; Survival analysis; Population; Inference; Regression; Hazard ratio; Relative risk; Econometrics; Mathematics; Medicine; Computer science; Cancer registry; Confidence interval; Artificial intelligence","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001482947,0.0002484838,0.0005308972,0.0002038063,0.000142207,0.00001004023,0.0001186909,0.000111248,0.0003816466],"category_scores_gemma":[0.01487093,0.0001903738,0.00002823142,0.0003975341,0.0001967803,0.00005924466,0.00004931229,0.0004175043,0.00001495951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001691302,"about_ca_system_score_gemma":0.0002979699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004748666,"about_ca_topic_score_gemma":0.00005157184,"domain_scores_codex":[0.9976715,0.0001705534,0.0007467399,0.0003898285,0.0006582943,0.0003630885],"domain_scores_gemma":[0.9973927,0.001489715,0.0001780198,0.0003276194,0.0003778431,0.0002341294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005545414,0.000122943,0.0001891609,0.00006668979,0.00001954003,0.00002345581,0.0008050483,0.0217655,0.0007784858,0.9684623,0.005881669,0.001829741],"study_design_scores_gemma":[0.0004269994,0.00008483411,0.00009475854,0.0001808269,0.00004224116,0.000003922069,0.0001497743,0.5011151,0.00002078422,0.4977294,0.00003092797,0.0001204535],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006207952,0.00001971881,0.9852509,0.0002421484,0.0003507418,0.0003257646,0.000489133,0.00002849016,0.00708518],"genre_scores_gemma":[0.08356802,0.00001106918,0.9144393,0.0001696774,0.00007823875,0.00002786184,0.00003688078,0.00003567001,0.001633261],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4793496,"threshold_uncertainty_score":0.9934272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1799157385617278,"score_gpt":0.4606334294214673,"score_spread":0.2807176908597395,"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."}}