{"id":"W1931398059","doi":"10.1002/cjs.11256","title":"Efficient semiparametric mixture inferences on cure rate models for competing risks","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Health Canada; National Institutes of Health","keywords":"Nonparametric statistics; Statistics; Semiparametric regression; Multinomial distribution; Econometrics; Soft tissue sarcoma; Cancer; Multinomial logistic regression; Mixture model; Medicine; Proportional hazards model; Mathematics; Internal medicine; Sarcoma; Pathology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.07426152,0.002216241,0.005349215,0.003854543,0.0009319173,0.003321113,0.004438668,0.002836895,0.007786215],"category_scores_gemma":[0.1739235,0.002783744,0.004932696,0.002353141,0.003578267,0.00457193,0.005118289,0.005382773,0.001104963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003278401,"about_ca_system_score_gemma":0.002598422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005795527,"about_ca_topic_score_gemma":0.004164213,"domain_scores_codex":[0.9523345,0.04093319,0.0008918297,0.003045673,0.001974492,0.0008203136],"domain_scores_gemma":[0.7600228,0.2214593,0.007975891,0.006994596,0.002399209,0.001148294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001195538,0.0003342866,0.007350092,0.0004679159,0.001010017,0.0003573593,0.000541513,0.6479368,0.0007772923,0.2791517,0.00222838,0.0586491],"study_design_scores_gemma":[0.00009878365,0.0001238211,0.001053061,0.00005678378,0.0001097287,0.00005430949,0.00003866336,0.8538544,0.0002549918,0.1435718,0.000733369,0.00005031129],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02092112,0.0003355536,0.9767144,0.0003831315,0.00003201849,0.0002671442,0.0003252539,0.0002411588,0.0007803194],"genre_scores_gemma":[0.5589268,0.0009551718,0.4294984,0.0005324567,0.0002455015,0.002272039,0.001758275,0.000287141,0.005524189],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.07426152,"threshold_uncertainty_score":0.3927372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4089803570344782,"score_gpt":0.4564198491778415,"score_spread":0.04743949214336335,"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."}}