{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01060862,0.001648551,0.003129628,0.002454448,0.0008082226,0.0022449,0.003567272,0.002621308,0.002624697],"category_scores_gemma":[0.03020296,0.001326541,0.00422113,0.002173074,0.001582715,0.002904957,0.00322573,0.003558537,0.0009072592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001043321,"about_ca_system_score_gemma":0.001637844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009440971,"about_ca_topic_score_gemma":0.005359848,"domain_scores_codex":[0.9962184,0.002115393,0.0001594292,0.0006363462,0.0005741299,0.0002964242],"domain_scores_gemma":[0.9879386,0.009152954,0.0009660433,0.0006973035,0.0009419789,0.0003029981],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004281461,0.0001318885,0.009435087,0.0002278299,0.0003313676,0.0003041195,0.0005232917,0.8470583,0.00150104,0.06988701,0.001821776,0.06835015],"study_design_scores_gemma":[0.00001486365,0.00002945498,0.0003577102,0.00001296407,0.00003377652,0.00003226398,0.00001354688,0.9847935,0.0001655362,0.01385178,0.0006758668,0.00001876611],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01864045,0.0006121843,0.9790766,0.000339829,0.00004404122,0.00005704855,0.0001934171,0.000411935,0.0006245069],"genre_scores_gemma":[0.6430671,0.001992285,0.3428941,0.0007368213,0.0003085406,0.0008144163,0.001998985,0.0004160423,0.007771645],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01060862,"threshold_uncertainty_score":0.05610436,"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."}}