Impact of Improved Diagnosis and Treatment on Holistic CKD Burden
Bibliographic record
Abstract
Introduction: Chronic kidney disease (CKD) is an underdiagnosed and undertreated disease despite the availability of effective interventions. The potential clinical, economic, and environmental impacts of increased diagnosis and improved adherence to guideline-directed medical therapies (GDMTs) recommended for patients with CKD are not well-understood. Methods: Eight country populations (Australia, Brazil, China, Germany, The Netherlands, Spain, UK, and USA) were simulated for 25 years using the IMPACT CKD model to compare burdens under various diagnosis and GDMT adherence scenarios versus current practice. GDMT consisted of kidney protecting, glucose lowering, lipid lowering, as well as antihypertensive and lifestyle interventions. Patients could be treated with 1 or multiple therapies, if eligible, and no guideline changes occurred over the time horizon. Treatment effects were assumed multiplicative. Results: Scenarios with improved GDMT adherence projected cumulative decreases in dialysis, cardiovascular (CV) events, and death by -3.2% to -23.2%, -12.2% to -41.4%, and -2.3% to -9.3%, respectively, compared with current practice over 10 years. Because of delayed CKD progression, kidney replacement therapy (KRT) costs and environmental burden were also projected to decrease by -2.5% to -19.4% and -2.7% to -21.2%, respectively, compared with current practice. All treatment scenarios predicted greater improvements over 25 years, underscoring the long-term impact of CKD, and highlighting the importance of early intervention. Conclusion: Differences in projected impacts between countries are multifactorial, though they are sensitive to demographics and health care systems. Implementation of policies that lead to improved detection and treatment of CKD are urgently required across the globe to mitigate the growing burdens of CKD on patients and caregivers, health care systems, society, and our environment.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".