{"id":"W2082197682","doi":"10.1177/0962280213491641","title":"Expectation maximization-based likelihood inference for flexible cure rate models with Weibull lifetimes","year":2013,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Advancements in Photolithography Techniques","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Weibull distribution; Maximization; Expectation–maximization algorithm; Computer science; Maximum likelihood; Econometrics; Statistics; Artificial intelligence; Mathematics; Mathematical optimization","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01552268,0.001004497,0.001754088,0.001431234,0.00061942,0.001284602,0.002480433,0.001177215,0.001600562],"category_scores_gemma":[0.03802015,0.001040656,0.001556562,0.001624144,0.001884123,0.002156215,0.001871366,0.002699381,0.0004316455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001298476,"about_ca_system_score_gemma":0.001360837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00337531,"about_ca_topic_score_gemma":0.003113498,"domain_scores_codex":[0.996064,0.002899653,0.0001697127,0.0004026475,0.0003162455,0.0001478126],"domain_scores_gemma":[0.9688575,0.02803173,0.001288484,0.001125913,0.0005246666,0.0001717851],"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":[0.00009367044,0.00005363868,0.001985281,0.0001150227,0.0001129595,0.0001684453,0.0001660644,0.8550961,0.0007769801,0.1026497,0.0005933279,0.03818879],"study_design_scores_gemma":[0.0000123871,0.00001411373,0.0002769597,0.0000140467,0.00001068867,0.00004141783,0.00001281374,0.952152,0.0002740813,0.04679703,0.0003746541,0.00001985082],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004361744,0.000150935,0.9951127,0.00008765371,0.000006290018,0.00001816266,0.00003760904,0.00007495081,0.0001500067],"genre_scores_gemma":[0.3705288,0.001021477,0.624353,0.0002106543,0.0000922871,0.0004348992,0.0005796757,0.0002303423,0.002549004],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01552268,"threshold_uncertainty_score":0.08209282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06775448943020891,"score_gpt":0.4855262341241343,"score_spread":0.4177717446939254,"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."}}