{"id":"W7132981649","doi":"","title":"Improving Mixture Cure Modelling of Multiple Molecular Factors in Cancer Prognosis","year":2023,"lang":"","type":"dissertation","venue":"TSpace","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Canadian Institutes of Health Research; Alliance de recherche numérique du Canada; Government of Ontario; Compute Canada","keywords":"Covariate; Imputation (statistics); Missing data; Confidence interval; Proportional hazards model; Nominal level; Likelihood-ratio test; Maximum likelihood; Sample size determination","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004752359,0.000665987,0.001227222,0.0003337708,0.00008711779,0.00005654345,0.0003817422,0.0007919789,0.000266296],"category_scores_gemma":[0.003030682,0.0006353502,0.0002678747,0.0008498906,0.00007361162,0.00006384118,0.00008273326,0.001014575,0.000008639418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001444714,"about_ca_system_score_gemma":0.0003419131,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0106212,"about_ca_topic_score_gemma":0.001116984,"domain_scores_codex":[0.9966185,0.0002565446,0.001071522,0.000769167,0.000630952,0.0006533108],"domain_scores_gemma":[0.9957644,0.002238138,0.0009428043,0.0004494673,0.0004411208,0.0001640536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009289362,0.002736807,0.04681588,0.07162446,0.00148474,0.0002370408,0.385475,0.08389673,0.2709116,0.04673791,0.0004103083,0.08874061],"study_design_scores_gemma":[0.0008018774,0.0002087877,0.001737733,0.00580339,0.0005682218,4.418265e-7,0.03082933,0.8066813,0.1332392,0.01891035,0.00002100397,0.001198326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7035624,0.000720773,0.2937415,0.00004878609,0.0005776403,0.000988484,0.0001358969,0.00005476283,0.0001697039],"genre_scores_gemma":[0.8091372,0.0005665423,0.1883376,0.00001029436,0.00005721513,0.000259181,0.0001102203,0.0002020116,0.001319713],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7227846,"threshold_uncertainty_score":0.9996098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.12171245554895,"score_gpt":0.4176991866217735,"score_spread":0.2959867310728235,"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."}}