{"id":"W1524113978","doi":"10.1002/bimj.201100013","title":"Incorporating temporal features of repeatedly measured covariates into tree‐structured survival models","year":2012,"lang":"en","type":"article","venue":"Biometrical Journal","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Centre for Addiction and Mental Health","funders":"National Cancer Institute; National Institute of Mental Health","keywords":"Covariate; Tree (set theory); Statistics; Recursive partitioning; Random forest; Survival analysis; Random effects model; Proportional hazards model; Computer science; Baseline (sea); Mathematics; Artificial intelligence; Medicine; Meta-analysis","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003071138,0.0002307574,0.0006260517,0.0007220097,0.0001906983,0.00006177458,0.0003191317,0.0002343644,0.0001078903],"category_scores_gemma":[0.01246973,0.0001617279,0.0001668435,0.001982291,0.0001456319,0.0002455125,0.0001077388,0.0005948847,0.000003515394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001072777,"about_ca_system_score_gemma":0.0001071245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007528246,"about_ca_topic_score_gemma":0.000004249807,"domain_scores_codex":[0.9968548,0.0006822031,0.0009434847,0.0001839891,0.0008799316,0.0004555982],"domain_scores_gemma":[0.9958577,0.002280134,0.0006755397,0.0002442843,0.0005235689,0.0004188357],"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":[0.0004236657,0.0007167403,0.06898768,0.0002645649,0.0004910613,0.00003863241,0.001792378,0.00001106364,0.03430561,0.6963096,0.004090766,0.1925683],"study_design_scores_gemma":[0.000949154,0.0002785459,0.04728673,0.00008887151,0.0001209888,0.000126796,0.0003199436,0.0008594836,0.004517656,0.9450047,0.0001040226,0.0003430797],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09261674,0.0009243259,0.9010584,0.0001402992,0.001243894,0.0001990448,0.00003440322,0.00006261127,0.003720259],"genre_scores_gemma":[0.5590074,0.000008076436,0.4406093,0.00001662024,0.0003118836,0.000001503599,0.000002007858,0.00001609795,0.00002710149],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4663907,"threshold_uncertainty_score":0.9958487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1473938680889569,"score_gpt":0.3780216939349325,"score_spread":0.2306278258459756,"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."}}