{"id":"W2271845266","doi":"10.1093/biostatistics/kxv042","title":"Sieve estimation in a Markov illness-death process under dual censoring","year":2015,"lang":"en","type":"article","venue":"Biostatistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Censoring (clinical trials); Estimator; Statistics; Inference; Sieve (category theory); Imputation (statistics); Markov chain; Mathematics; Computer science; Parametric statistics; Econometrics; Missing data; 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":[],"consensus_categories":[],"category_scores_codex":[0.0005619571,0.000181662,0.0002730815,0.00008992495,0.00004124987,0.00005352995,0.000109784,0.00009562835,0.00005968796],"category_scores_gemma":[0.007502854,0.0001648065,0.00001898178,0.0002377842,0.00005216646,0.00007884058,0.00004618249,0.0001720016,0.00003692761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001143641,"about_ca_system_score_gemma":0.0001405729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004518511,"about_ca_topic_score_gemma":0.00002128521,"domain_scores_codex":[0.9985339,0.0001257396,0.0004154838,0.0002524147,0.000356859,0.0003155774],"domain_scores_gemma":[0.9979896,0.001292411,0.0001268841,0.0002106075,0.0002116549,0.0001688407],"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":[0.00005592259,0.0001606199,0.001662378,0.0002005479,0.00001444108,0.00006584769,0.001335442,0.0001686059,0.00005358365,0.9712963,0.001400593,0.02358576],"study_design_scores_gemma":[0.0006347391,0.000106064,0.005364156,0.0001038165,0.00002963147,0.00001710322,0.0008187909,0.04797777,0.0003801388,0.9441546,0.0001085157,0.0003046395],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06907449,0.00002703082,0.9260029,0.0001398026,0.0002918675,0.0002666778,0.0001455542,0.00007926928,0.003972454],"genre_scores_gemma":[0.5060496,0.000003131111,0.4937262,0.0000556388,0.00003497371,0.00001705831,0.00001065147,0.00001976292,0.00008304176],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4369751,"threshold_uncertainty_score":0.8982159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1501214199318977,"score_gpt":0.4187033755424467,"score_spread":0.268581955610549,"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."}}