Current Management Strategies of Chronic Kidney Disease in Resource-Limited Countries
Bibliographic record
Abstract
Oluwatoyin Idaomeh Ameh,1 Udeme Ekrikpo,2 Aminu Bello,3 Ikechi Okpechi3– 5 1Division of Nephrology, Zenith Medical & Kidney Centre, Gudu, Abuja, Nigeria; 2Renal Unit, Department of Internal Medicine, University of Uyo, Uyo, Nigeria; 3Division of Nephrology and Immunology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada; 4Division of Nephrology and Hypertension, University of Cape Town, Cape Town, South Africa; 5Kidney and Hypertension Research Unit, University of Cape Town, Cape Town, South AfricaCorrespondence: Ikechi OkpechiDivision of Nephrology and Hypertension, University of Cape Town, Cape Town, South AfricaCorrespondence Email Ikechi.Okpechi@uct.ac.zaAbstract: The incidence and prevalence of chronic kidney disease (CKD) and kidney failure continues to increase worldwide, especially in resource-limited countries. Many countries in this category already have a massive burden of communicable diseases, as well as socio-economic and socio-demographic challenges. The rising CKD burden and exorbitant economic cost associated with treatment are mainly responsible for the alarming mortality rate associated with kidney disease in these regions. There is often poor risk factor (diabetes and hypertension) and CKD awareness in these countries and limited availability and affordability of treatment options. Given these observations, early disease detection and preventive measures remain the best options for disease management in resource-limited settings. Primary, secondary and tertiary preventive strategies need to be enhanced and should particularly include measures to increase awareness, regular assessment to detect hypertension, diabetes and albuminuria, options for early referral of identified patients to a nephrologist and options for conservative kidney management where kidney replacement therapies may not be available or indicated. Much is still needed to be done by governments in these regions, especially regarding healthcare funding, improving the primary healthcare systems and enhancing non-communicable disease detection and treatment programs as these will have effects on kidney care in these regions.Keywords: chronic kidney disease, LMICs, kidney replacement therapy, non-communicable diseases, early detection and prevention, early referral
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".