{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005597597,0.0004657904,0.000451339,0.0004238021,0.0004473822,0.0011945,0.0008062215,0.001067203,0.003518197],"category_scores_gemma":[0.00202692,0.0003515742,0.001056775,0.0006812565,0.0003905033,0.0005922111,0.0008760652,0.0009325498,0.0003372756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001963645,"about_ca_system_score_gemma":0.003297922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2540873,"about_ca_topic_score_gemma":0.1639382,"domain_scores_codex":[0.9997976,0.00008904161,0.00000819337,0.00003599895,0.00002242073,0.0000467194],"domain_scores_gemma":[0.9993444,0.0003045833,0.00007363925,0.00003133177,0.0001337846,0.0001122177],"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":[0.00002443957,0.00002042192,0.004954235,0.00001255954,0.00003133113,0.00002571971,0.00002182622,0.9901542,0.00007299817,0.002381807,0.0006474457,0.001653002],"study_design_scores_gemma":[0.000008014665,0.00001018188,0.001198176,0.000006542277,0.00001046786,0.000005228274,0.00004160859,0.996877,0.00003182541,0.001169895,0.0006344704,0.000006662411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8673463,0.0006578639,0.09661147,0.005111085,0.000216198,0.0001347229,0.008919439,0.0003986958,0.02060408],"genre_scores_gemma":[0.9817612,0.0003303791,0.01178725,0.0001968777,0.0000412368,0.0001083422,0.002192845,0.00004414437,0.003537801],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2540873,"threshold_uncertainty_score":0.5052167,"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."}}