{"id":"W2492360548","doi":"10.4137/cin.s39364","title":"Recursive Partitioning Method on Competing Risk Outcomes","year":2016,"lang":"en","type":"article","venue":"Cancer Informatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Waterloo; Public Health Ontario; University of Toronto","funders":"","keywords":"Recursive partitioning; Proportional hazards model; Confounding; Computer science; Tree (set theory); Covariate; Pruning; Parametric statistics; Predictive power; Data mining; Econometrics; Machine learning; Statistics; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001926731,0.0001020959,0.00009800421,0.00003635209,0.0001037337,0.00001914747,0.0001209871,0.00007272067,0.00006874884],"category_scores_gemma":[0.0001448426,0.00006616508,0.00005778514,0.00005743101,0.0000307972,0.00000796972,0.00004104291,0.00005691503,0.00004079031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003636044,"about_ca_system_score_gemma":0.00005513909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004919158,"about_ca_topic_score_gemma":0.000005809669,"domain_scores_codex":[0.999325,0.00004815353,0.0002435934,0.0001059407,0.0001216042,0.0001556615],"domain_scores_gemma":[0.9993312,0.00003555253,0.0002281999,0.0002625162,0.00008063882,0.00006192258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002381137,0.00008723408,0.1040104,0.00008809752,0.0002062771,6.499342e-7,0.002264919,0.002262869,0.1827282,0.004274263,0.06002086,0.6438181],"study_design_scores_gemma":[0.001492528,0.0002038857,0.01770013,0.0002171502,0.00003948257,0.000002719473,0.001341202,0.0003186696,0.4862228,0.0005722006,0.4914794,0.0004097594],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5807261,0.0003144702,0.3971501,0.001916308,0.001092242,0.0003757225,0.0001577249,0.00006891825,0.01819841],"genre_scores_gemma":[0.9831255,0.0009545023,0.01204774,0.001897312,0.0002492271,0.0001333602,0.00003590963,0.00002068547,0.001535735],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6434083,"threshold_uncertainty_score":0.2698134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01973672242376909,"score_gpt":0.3270239789079975,"score_spread":0.3072872564842284,"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."}}