Screening Programs for Early Detection of CKD: A Systematic Literature Review
Bibliographic record
Abstract
Background: Early detection of chronic kidney disease (CKD) allows intervention to delay progression and other adverse outcomes. Kidney Disease: Improving Global Outcomes (KDIGO) guidelines advise screening high-risk groups with albumin-to-creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR); diagnostic if abnormality in one/both for ≥ 3 months. We investigated CKD screening programs in the US, Canada, Australia, and UK. Methods: Systematic literature review (SLR) of CKD screening programs in patients with diabetes and/or hypertension between Jan 2018 – Oct 2023. Results: Of 2361 records screened, 52 full-text reports were assessed, and 23 publications (of 21 studies) included. In addition to diabetes and/or hypertension (13 studies), high-risk groups included indigenous populations (4 studies), underserved areas (3 studies) and older population (1 study). Of the 21 studies, 5 reported screening prevalence and 16 described screening programs. Also, 7 studies reported 1 test (ACR or eGFR), 9 used ACR + eGFR, 5 used ACR + serum creatinine. Of the 16 screening programs, 9 were in community care and 7 in primary care. Prevalence (mean weighted) of screening in high-risk patients was 4-fold greater in community vs. primary care. Low screening rates were reported for patients with hypertension and diabetes (Fig. 1). Of 10 studies reporting assessment frequency, only 3 repeated ACR within 1 year. Conclusion: This SLR suggests a low prevalence of CKD screening of high-risk patients, particularly in primary care. Contrary to KDIGO guidelines, approximately one-third of studies performed incomplete screening (only 1 test); follow-up testing was infrequent or not reported. Inadequate testing for CKD and lack of adherence to KDIGO guidelines are delaying CKD diagnosis and appropriate early therapy. Funding: Commercial Support - Boehringer Ingelheim Pharmaceuticals, Inc. (BIPI) & Lilly, USA LLC
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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 | 0.010 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".