{"id":"W3152931546","doi":"10.1016/j.ekir.2021.03.338","title":"POS-322 INSIDE CKD: PROJECTING THE FUTURE BURDEN OF CHRONIC KIDNEY DISEASE IN THE AMERICAS AND THE ASIA-PACIFIC REGION USING MICROSIMULATION MODELLING","year":2021,"lang":"en","type":"article","venue":"Kidney International Reports","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; Orthopaedic Innovation Centre","funders":"","keywords":"Kidney disease; Microsimulation; Medicine; Public health; Disease burden; Disease; Environmental health; Gerontology; Intensive care medicine; Internal medicine; Pathology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002993338,0.0001428368,0.0002000013,0.0001860209,0.0002945788,0.0003626868,0.0003430447,0.00005441766,0.00002149945],"category_scores_gemma":[0.003911474,0.00007348071,0.0001513988,0.000745158,0.0001986424,0.000303131,0.0001082341,0.0002425359,0.000001201772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006999513,"about_ca_system_score_gemma":0.0005516832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004463228,"about_ca_topic_score_gemma":0.00002541558,"domain_scores_codex":[0.9966917,0.000424513,0.0009303972,0.0004571208,0.001327783,0.0001684771],"domain_scores_gemma":[0.9971063,0.0007311079,0.0008500771,0.0005941069,0.0006169698,0.0001014983],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003855454,0.0001703277,0.04238604,0.00004547086,0.0001398459,0.0005046558,0.01867336,0.9069414,0.002170835,0.008717733,0.003609747,0.01625499],"study_design_scores_gemma":[0.0003980953,0.000009726045,0.001501155,0.0001383603,0.00004473293,0.0003431425,0.007034172,0.9560748,0.00006690335,0.02251398,0.01177027,0.0001046108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9011294,0.001221477,0.04336938,0.04967308,0.001529029,0.0006129024,0.00002181027,0.00002598865,0.002416876],"genre_scores_gemma":[0.9980076,0.0002241077,0.000480349,0.0006254337,0.0003723041,0.00001794712,0.00004529969,0.00001151353,0.0002154488],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09687814,"threshold_uncertainty_score":0.4682682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07034840898746701,"score_gpt":0.3532322609815083,"score_spread":0.2828838519940413,"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."}}