{"id":"W6945043939","doi":"10.25384/sage.21454222","title":"sj-pdf-1-smm-10.1177_09622802221134172 - Supplemental material for Bayesian inference for Cox proportional hazard models with partial likelihoods, nonlinear covariate effects and correlated observations","year":2022,"lang":"en","type":"article","venue":"Sage Journals Data","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Covariate; Bayesian probability; Proportional hazards model; Inference; Hazard; Bayesian inference; Statistical inference; Nonlinear system","routes":{"ca_aff":true,"ca_fund":false,"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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001654406,0.0003105542,0.0005390163,0.0000990476,0.0008455266,0.0002804148,0.000584131,0.0000894532,0.008751005],"category_scores_gemma":[0.001297628,0.0002635084,0.00006654821,0.000210131,0.0001120014,0.0005399848,0.0005182569,0.0003314957,0.000002959257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000852966,"about_ca_system_score_gemma":0.0004298983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003549544,"about_ca_topic_score_gemma":0.00003929737,"domain_scores_codex":[0.9972377,0.0002973211,0.0008197735,0.0005805226,0.0005431701,0.0005214785],"domain_scores_gemma":[0.9963365,0.00210597,0.0005162222,0.0005976396,0.0002180502,0.0002256546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.01825638,0.006472017,0.01017686,0.005987971,0.004747691,0.0006083974,0.00338094,0.001265799,0.01963768,0.4508153,0.257068,0.221583],"study_design_scores_gemma":[0.005050112,0.001538474,0.000491945,0.0004077784,0.0006291616,0.0001403029,0.0001852066,0.4313138,0.0005889144,0.5527672,0.006277618,0.0006094076],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003421565,0.00009316741,0.9290338,0.0006934087,0.0003698145,0.001863443,0.06445081,0.00005069308,0.00002331777],"genre_scores_gemma":[0.02128755,0.00006336652,0.9699333,0.0002248689,0.0003285668,0.0007027343,0.007237406,0.00006507137,0.0001571173],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.430048,"threshold_uncertainty_score":0.9999817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1201039679254812,"score_gpt":0.3829423885821843,"score_spread":0.2628384206567032,"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."}}