{"id":"W2021427999","doi":"10.1159/000356088","title":"Monitoring Dialysis Outcomes across the World - The MONDO Global Database Consortium","year":2013,"lang":"en","type":"article","venue":"Blood Purification","topic":"Dialysis and Renal Disease Management","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Dialysis; Medicine; Limiting; Descriptive statistics; Database; Internal medicine; Computer science; Statistics; 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":[0.0003475873,0.0001350806,0.0001855577,0.00003098582,0.0002678824,0.0001752475,0.000244268,0.00002581273,0.0001428751],"category_scores_gemma":[0.0001169386,0.00006405106,0.000173401,0.0004662868,0.000101803,0.000119084,0.00008887962,0.00009832774,0.0003162884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003013142,"about_ca_system_score_gemma":0.00002242182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003515832,"about_ca_topic_score_gemma":0.00004142019,"domain_scores_codex":[0.9987611,0.00008304859,0.0002893684,0.0002479601,0.0003831618,0.0002353259],"domain_scores_gemma":[0.9986818,0.00008989073,0.0001304729,0.00089077,0.0001076945,0.00009930474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004790092,0.00113348,0.9152959,0.00009202011,0.001718431,0.00001240653,0.000588653,0.00005712172,0.004137759,0.003858318,0.01254915,0.06050889],"study_design_scores_gemma":[0.0006230253,0.00001237605,0.9850872,0.00002977137,0.001190915,0.00000178549,0.0006091048,0.0001512019,0.00220405,0.0001161235,0.009878283,0.00009617465],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9748569,0.001097423,0.000154094,0.01765086,0.0002930148,0.001024709,0.00003854291,0.00007494734,0.00480955],"genre_scores_gemma":[0.9947476,0.0001159662,0.000180954,0.0005574989,0.0002892058,0.0003358147,0.0000583657,0.00001014154,0.003704458],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06979132,"threshold_uncertainty_score":0.4065354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.021998638094759,"score_gpt":0.3056238211322869,"score_spread":0.2836251830375279,"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."}}