{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0006557386,0.00008436091,0.0002483404,0.00004698926,0.0001559709,0.00008895124,0.0003091254,0.00004205554,0.0003342667],"category_scores_gemma":[0.01136862,0.00008003449,0.00003325721,0.0001011918,0.00007030708,0.00006719491,0.00002749939,0.0001732224,0.000002936418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007235035,"about_ca_system_score_gemma":0.00195746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004134193,"about_ca_topic_score_gemma":0.01328067,"domain_scores_codex":[0.9989892,0.00008389624,0.000426269,0.0001121923,0.0001377828,0.0002506785],"domain_scores_gemma":[0.9955974,0.002747475,0.000183269,0.0003063323,0.0007044246,0.0004610714],"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":[0.000003200662,0.0000123028,0.0001457145,0.00005203055,0.00003850867,0.0001832859,0.00008652176,0.00000158455,0.00002761888,0.9545044,0.0347187,0.01022612],"study_design_scores_gemma":[0.000237201,0.00004300854,0.0006783013,0.00006030525,0.00010254,0.0001047157,0.0002823918,0.0004543152,0.0000549434,0.9749741,0.02290383,0.0001043597],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008604737,0.00009552644,0.9751987,0.00037291,0.0007108441,0.0000495537,0.02224183,0.000002807888,0.0004673689],"genre_scores_gemma":[0.02771987,0.00001282496,0.971673,0.00006278059,0.0001968898,9.490978e-7,0.0001798651,0.00001512654,0.0001386625],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0268594,"threshold_uncertainty_score":0.996959,"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."}}