{"id":"W2055175121","doi":"10.1093/gerona/glv042","title":"Calculating the Rate of Senescence From Mortality Data: An Analysis of Data From the ERA-EDTA Registry","year":2015,"lang":"en","type":"article","venue":"The Journals of Gerontology Series A","topic":"Genetics, Aging, and Longevity in Model Organisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Research Innovation and Development Trust, University of Malta; European Renal Association-European Dialysis and Transplant Association; Universidade Paranaense","keywords":"Senescence; Mortality rate; Demography; Dialysis; Medicine; Gerontology; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0160195,0.0004172555,0.0009019352,0.003140235,0.0002134143,0.0006851347,0.0005508557,0.0004532298,0.0004453827],"category_scores_gemma":[0.0212652,0.0002395361,0.001782175,0.003223524,0.0002219427,0.0008311448,0.001136557,0.0005955887,0.0001990671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005532377,"about_ca_system_score_gemma":0.0006875697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003253569,"about_ca_topic_score_gemma":0.002821606,"domain_scores_codex":[0.9935071,0.004178184,0.0006430644,0.0007969289,0.0007042706,0.0001704483],"domain_scores_gemma":[0.9789069,0.01318632,0.003748368,0.00269566,0.001209099,0.0002536559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005178718,0.00006599943,0.9837848,0.0001124132,0.0006137938,0.00006977528,0.0002313355,0.00364579,0.0003749539,0.0002398627,0.0003891866,0.009954078],"study_design_scores_gemma":[0.00003656119,0.0003070361,0.9717496,0.00003528537,0.0003026305,0.0002966984,0.0002456526,0.02505734,0.0005757378,0.0002895668,0.001072837,0.00003108665],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910467,0.0004962471,0.005071227,0.00005768541,0.0000122264,0.00005201433,0.002978553,0.00005579301,0.0002296094],"genre_scores_gemma":[0.987215,0.0002946219,0.006970078,0.00002950087,0.0000186291,0.00009219985,0.005243344,0.00002993416,0.0001067168],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0160195,"threshold_uncertainty_score":0.08472025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2142216953632111,"score_gpt":0.3819989282838114,"score_spread":0.1677772329206003,"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."}}