{"id":"W3005064834","doi":"10.1002/bimj.201900042","title":"Nonlinear and time‐dependent effects of sparsely measured continuous time‐varying covariates in time‐to‐event analysis","year":2020,"lang":"en","type":"article","venue":"Biometrical Journal","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University Health Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Covariate; Nonlinear system; Statistics; Event (particle physics); Mathematics; Discrete time and continuous time; Econometrics; Computer science; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001465288,0.0001951397,0.0009185362,0.001156197,0.0000534858,0.00009500095,0.0002690312,0.0001151196,0.0007148589],"category_scores_gemma":[0.01599515,0.0001551423,0.0001644815,0.004071996,0.00005069421,0.00005193975,0.0001403036,0.0003106582,0.0001615899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006087716,"about_ca_system_score_gemma":0.0000485071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001174549,"about_ca_topic_score_gemma":1.505279e-7,"domain_scores_codex":[0.9974316,0.0004496732,0.000780563,0.0002807839,0.0007396094,0.0003177423],"domain_scores_gemma":[0.9951882,0.003684282,0.0002882096,0.000138045,0.0001962677,0.0005049777],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002305725,0.005429935,0.03354804,0.001322896,0.007201206,0.00178092,0.002452365,0.0003611056,0.6275314,0.002766496,0.007186912,0.3081129],"study_design_scores_gemma":[0.01921254,0.01293774,0.11765,0.00170948,0.009873406,0.0004237957,0.0001166474,0.6221834,0.1475475,0.06262472,0.001879825,0.003840908],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.31326,0.0006751309,0.6835378,0.001065025,0.0001045689,0.0006343183,0.00009700174,0.00005743651,0.0005687505],"genre_scores_gemma":[0.4955784,0.00003642184,0.5037589,0.0002728902,0.0001638019,0.000005864275,0.000003850032,0.00003171999,0.0001481571],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6218224,"threshold_uncertainty_score":0.9922935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07498559534496042,"score_gpt":0.3337489972951085,"score_spread":0.2587634019501481,"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."}}