{"id":"W3211638954","doi":"10.1002/cjs.11641","title":"Matching distributions for survival data","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Covariate; Censoring (clinical trials); Estimator; Quantile; Statistics; Econometrics; Matching (statistics); Survival analysis; Quantile regression; Accelerated failure time model; Computer science; Regression; Consistency (knowledge bases); Mathematics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.05653997,0.001356069,0.002928792,0.006045313,0.001297286,0.003481013,0.004999784,0.004799064,0.01479771],"category_scores_gemma":[0.2287069,0.001087933,0.003040238,0.006419986,0.003640422,0.006876496,0.005574871,0.006086842,0.005160384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00277344,"about_ca_system_score_gemma":0.002402839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002295221,"about_ca_topic_score_gemma":0.001056796,"domain_scores_codex":[0.9760099,0.01520444,0.0011799,0.003638716,0.003288634,0.0006784095],"domain_scores_gemma":[0.8938661,0.08052318,0.006428247,0.01350496,0.004668864,0.00100862],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004328261,0.0002210219,0.011741,0.0006892488,0.0003258341,0.0005431554,0.0008246114,0.1089378,0.001113994,0.6413075,0.009308121,0.2245549],"study_design_scores_gemma":[0.0001299546,0.0001428373,0.002416774,0.0002076715,0.00006336228,0.0003348286,0.0001668054,0.3413343,0.0008211994,0.6422907,0.01201782,0.00007367129],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003485172,0.0003450753,0.9936692,0.0003229568,0.00007200988,0.0002813904,0.0004613298,0.0005484964,0.0008142582],"genre_scores_gemma":[0.2598171,0.001921149,0.7178001,0.001022986,0.0005286883,0.005405655,0.005550226,0.0008932377,0.007060847],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05653997,"threshold_uncertainty_score":0.2990155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3067654523920212,"score_gpt":0.3965397158416624,"score_spread":0.08977426344964118,"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."}}