Chronic kidney disease in low-income to middle-income countries: the case for increased screening
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it