{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009887858,0.001125689,0.001245389,0.002001494,0.000575246,0.001231447,0.002896708,0.001176764,0.004829213],"category_scores_gemma":[0.02540821,0.000493849,0.001692772,0.00173407,0.0009063606,0.001558813,0.002076136,0.002114767,0.001206209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009284928,"about_ca_system_score_gemma":0.001559966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003291511,"about_ca_topic_score_gemma":0.002981916,"domain_scores_codex":[0.9943576,0.004020515,0.0001647352,0.0005376963,0.0007212939,0.0001980917],"domain_scores_gemma":[0.9848529,0.01202641,0.0005921326,0.001078625,0.001238574,0.0002112568],"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":[0.0002476699,0.0001143174,0.005499563,0.0004187499,0.0003038407,0.000446779,0.0006253396,0.2394651,0.002396278,0.4178391,0.009226757,0.3234166],"study_design_scores_gemma":[0.00005569388,0.00009217498,0.001069261,0.00006129214,0.00006038599,0.000255819,0.00004505344,0.7989385,0.0007563969,0.1919497,0.006676889,0.00003879851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002445519,0.0001761566,0.99642,0.0001179368,0.00002487799,0.00009592743,0.00009951671,0.0001479241,0.0004720595],"genre_scores_gemma":[0.1549871,0.0007277047,0.8362439,0.0002702044,0.0002495482,0.001588664,0.001464964,0.0003473149,0.004120639],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009887858,"threshold_uncertainty_score":0.05229264,"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."}}