Chronic kidney disease in low-income to middle-income countries: the case for increased screening
Bibliographic record
Abstract
Chronic kidney disease (CKD) is fast becoming a major public health issue, disproportionately burdening low-income to middle-income countries, where detection rates remain low. We critically assessed the extant literature on CKD screening in low-income to middle-income countries. We performed a PubMed search, up to September 2016, for studies on CKD screening in low-income to middle-income countries. Relevant studies were summarised through key questions derived from the Wilson and Jungner criteria. We found that low-income to middle-income countries are ill-equipped to deal with the devastating consequences of CKD, particularly the late stages of the disease. There are acceptable and relatively simple tools that can aid CKD screening in these countries. Screening should primarily include high-risk individuals (those with hypertension, type 2 diabetes, HIV infection or aged >60 years), but also extend to those with suboptimal levels of risk (eg, prediabetes and prehypertension). Since screening for hypertension, type 2 diabetes and HIV infection is already included in clinical practice guidelines in resource-poor settings, it is conceivable to couple this with simple CKD screening tests. Effective implementation of CKD screening remains a challenge, and the cost-effectiveness of such an undertaking largely remains to be explored. In conclusion, for many compelling reasons, screening for CKD should be a policy priority in low-income to middle-income countries, as early intervention is likely to be effective in reducing the high burden of morbidity and mortality from CKD. This will help health systems to achieve cost-effective prevention.
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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 | 0.062 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.019 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 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".