Early Detection of CKD: Implications for Low-Income, Middle-Income, and High-Income Countries
Bibliographic record
Abstract
CKD is common, costly, and associated with adverse health outcomes. Because inexpensive treatments can slow the rate of kidney function loss, and because CKD is asymptomatic until its later stages, the idea of early detection of CKD to improve outcomes ignites enthusiasm, especially in low- and middle-income countries where renal replacement is often unavailable or unaffordable. Available data and prior experience suggest that the benefits of population-based screening for CKD are uncertain; that there is potential for harms; that screening is not a wise use of resources, even in high-income countries; and that screening has substantial opportunity costs in low- and middle-income countries that offset its hypothesized benefits. In contrast, some of the factors that diminish the value of population-based screening (such as markedly higher prevalence of CKD in people with diabetes, hypertension, and cardiovascular disease, as well as high preexisting use of kidney testing in such patients) substantially increase the appeal of searching for CKD in people with known kidney risk factors (case finding) in high-income countries as well as in low- and middle-income countries. For both screening and case finding, detection of new cases is the easiest component; the real challenge is ensuring appropriate management for a chronic disease, usually for years or even decades. This review compares and contrasts the benefits, harms, and opportunity costs associated with these two approaches to early detection of CKD. We also suggest criteria (discussed separately for high-income countries and for low- and middle-income countries) to use in assessing when countries should consider case finding versus when they should consider foregoing systematic attempts at early detection and focus on management of known cases.
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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 | 0.003 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".