{"id":"W2749556736","doi":"10.1177/0962280217727314","title":"Survival forests for data with dependent censoring","year":2017,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"Russian Science Foundation","keywords":"Censoring (clinical trials); Estimator; Covariate; Survival analysis; Computer science; Accelerated failure time model; Survival function; Statistics; Kaplan–Meier estimator; Econometrics; Data mining; Mathematics; Machine learning","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","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.04271108,0.0002343928,0.0007038013,0.0001576869,0.0006199265,0.0002675056,0.002906909,0.0002591594,0.001347007],"category_scores_gemma":[0.5166262,0.0001664274,0.00003126006,0.0001709984,0.001633335,0.0001422704,0.001586676,0.001518926,0.00001534899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009803521,"about_ca_system_score_gemma":0.000542514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003557405,"about_ca_topic_score_gemma":0.001179782,"domain_scores_codex":[0.990856,0.003314421,0.0007236952,0.001015355,0.002776684,0.001313859],"domain_scores_gemma":[0.8813134,0.1146541,0.0001368946,0.002559473,0.0004782385,0.0008577947],"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":[0.0002029554,0.0001173126,0.001867167,0.0001931342,0.00001976548,0.0001547831,0.0000293727,9.407604e-8,0.00001897267,0.5327839,0.0006892779,0.4639232],"study_design_scores_gemma":[0.001400752,0.0003798419,0.01609585,0.0003180605,0.00002268078,0.00001249969,0.0001086892,0.04231053,0.0001549956,0.9365232,0.002428559,0.0002443545],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008121795,0.00003179532,0.9870389,0.001380987,0.0003246119,0.0007351151,0.0003529374,0.00003056146,0.009292979],"genre_scores_gemma":[0.01797498,0.00003795197,0.9811468,0.00003389324,0.00026384,0.0001994175,0.000021303,0.00005301804,0.000268759],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4739151,"threshold_uncertainty_score":0.9995659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6773318042169131,"score_gpt":0.6953959658900677,"score_spread":0.01806416167315461,"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."}}