{"id":"W3156920115","doi":"10.1111/biom.13479","title":"Feature screening with large‐scale and high‐dimensional survival data","year":2021,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Actua; Western University","funders":"National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; National Science Foundation","keywords":"Covariate; Computer science; Sample size determination; Dimension (graph theory); Big data; Variable (mathematics); Scale (ratio); Data mining; Feature (linguistics); Sample (material); Computation; Variables; Machine learning; Statistics; Mathematics; Algorithm","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":[],"consensus_categories":[],"category_scores_codex":[0.0005659353,0.0001123529,0.0002216709,0.0001769586,0.00009172771,0.00006517753,0.0001611339,0.00008329062,0.0001117173],"category_scores_gemma":[0.003531234,0.00008363098,0.00001290741,0.001814598,0.00005212293,0.000067054,0.0003833643,0.000148608,0.000004518683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000853958,"about_ca_system_score_gemma":0.00004851906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009006069,"about_ca_topic_score_gemma":0.00001841766,"domain_scores_codex":[0.9988691,0.00008921642,0.0001134962,0.0003410971,0.0003728982,0.0002142447],"domain_scores_gemma":[0.9973997,0.001757716,0.00005605371,0.000495306,0.0001748447,0.0001163449],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001713934,0.0009830212,0.05018319,0.0006091147,0.0003754559,0.0006247355,0.0002715283,0.000001789937,0.002706462,0.5527548,0.08018176,0.3111368],"study_design_scores_gemma":[0.01087692,0.001344741,0.3868775,0.0008831978,0.001135902,0.0006979583,0.002137359,0.07366543,0.007801022,0.2678807,0.2430969,0.003602352],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04575523,0.0006660839,0.9505816,0.0008250578,0.0002246945,0.00008429518,0.001018828,0.00005141346,0.0007927899],"genre_scores_gemma":[0.02253892,0.0000287917,0.9764169,0.0001110405,0.00008145599,0.000001402406,0.000127972,0.00001693091,0.0006765465],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3366943,"threshold_uncertainty_score":0.4227472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1567047904430262,"score_gpt":0.380795179429857,"score_spread":0.2240903889868309,"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."}}