{"id":"W4289783327","doi":"10.1038/s41592-022-01563-7","title":"Survival analysis—time-to-event data and censoring","year":2022,"lang":"en","type":"article","venue":"Nature Methods","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre","funders":"","keywords":"Censoring (clinical trials); Event data; Computational biology; Computer science; Event (particle physics); Survival analysis; Biology; Statistics; Data science; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.001061363,0.00009443455,0.0001576697,0.00007445386,0.0001160124,0.00002237287,0.0003583779,0.00008860388,0.0001339429],"category_scores_gemma":[0.0003498388,0.00009968503,0.00005476506,0.0002862003,0.00001465578,0.000001235524,0.00137487,0.0002346971,0.000002418215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001828613,"about_ca_system_score_gemma":0.00004308013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001381355,"about_ca_topic_score_gemma":0.00001687434,"domain_scores_codex":[0.9989681,0.0002090068,0.0001153923,0.0004356815,0.0001179187,0.0001539262],"domain_scores_gemma":[0.9990675,0.00007771473,0.00004127203,0.0006984389,0.00002938008,0.00008571092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003759417,0.0001864573,0.01034382,0.00002815102,0.00194572,0.00002479589,0.0001747343,0.01277886,0.726994,0.000702938,0.04602819,0.2004164],"study_design_scores_gemma":[0.0002037587,0.0000988661,0.004342485,8.51987e-7,0.0002108839,0.000005432966,0.00004391761,0.002007418,0.01094092,0.00006292837,0.9819003,0.0001822192],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6971207,0.03059444,0.253453,0.003050329,0.002863995,0.0008104425,0.003024169,0.00005741903,0.009025542],"genre_scores_gemma":[0.6827237,0.0003494631,0.3071713,0.002927135,0.0009375777,0.00004498952,0.001963262,0.00005812307,0.00382447],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9358721,"threshold_uncertainty_score":0.4065038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01856794042335268,"score_gpt":0.3790974832928449,"score_spread":0.3605295428694922,"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."}}