{"id":"W4315797072","doi":"10.17615/63ad-p767","title":"Analysis of survival data with cure fraction and variable selection: A pseudo-observations approach","year":2023,"lang":"en","type":"article","venue":"UNC Libraries","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Fraction (chemistry); Statistics; Selection (genetic algorithm); Variable (mathematics); Mathematics; Computer science; Chemistry; Artificial intelligence; Chromatography; Mathematical analysis","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":[],"consensus_categories":[],"category_scores_codex":[0.001930723,0.0001202755,0.0003918204,0.000612297,0.0002131232,0.0004232787,0.0005627637,0.00007262838,0.0002526803],"category_scores_gemma":[0.0009159062,0.00008623718,0.00003763811,0.009848136,0.0001834358,0.002490686,0.0003473019,0.0001065014,0.000009980426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001020531,"about_ca_system_score_gemma":0.0001305194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001188825,"about_ca_topic_score_gemma":0.00001448898,"domain_scores_codex":[0.9977725,0.0003181089,0.0003729706,0.0005549283,0.0008191017,0.000162379],"domain_scores_gemma":[0.997285,0.001537045,0.0001965419,0.0007581385,0.0001550721,0.00006822247],"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":[0.0002509109,0.0001853133,0.4131761,0.00002223173,0.001097303,0.000002466894,0.001493012,0.01393722,0.005826111,0.5455945,0.01613756,0.002277365],"study_design_scores_gemma":[0.0003923967,0.0001600362,0.162702,0.000009754964,0.0005435772,0.000004855406,0.005811169,0.7347248,0.001673124,0.08455065,0.009135379,0.0002921919],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06675521,0.0001638921,0.9188668,0.0005956678,0.0001929394,0.0002838395,0.0002250939,0.0002404385,0.01267614],"genre_scores_gemma":[0.03785801,0.00000565385,0.9587825,0.00006252524,0.00005474911,0.00001686474,0.0003078093,0.00001526992,0.002896644],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7207876,"threshold_uncertainty_score":0.4731703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2836926911873698,"score_gpt":0.4086968479530062,"score_spread":0.1250041567656364,"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."}}