Preventing CKD in Developed Countries
Bibliographic record
Abstract
Chronic kidney disease (CKD) is an important public health concern in developed countries because of both the number of people affected and the high cost of care when prevention strategies are not effectively implemented. Prevention should start at the governance level with the institution of multisectoral polices supporting sustainable development goals and ensuring safe and healthy environments. Primordial prevention of CKD can be achieved through implementation of measures to ensure healthy fetal (kidney) development. Public health strategies to prevent diabetes, hypertension, and obesity as risk factors for CKD are important. These approaches are cost-effective and reduce the overall noncommunicable disease burden. Strategies to prevent nontraditional CKD risk factors, including nephrotoxin exposure, kidney stones, infections, environmental exposures, and acute kidney injury (AKI), need to be tailored to local needs and epidemiology. Early diagnosis and treatment of CKD risk factors such as diabetes, obesity, and hypertension are key for primary prevention of CKD. CKD tends to occur more frequently and to progress more rapidly among indigenous, minority, and socioeconomically disadvantaged populations. Special attention is required to meet the CKD prevention needs of these populations. Effective secondary prevention of CKD relies on screening of individuals at risk to detect and treat CKD early, using established and emerging strategies. Within high-income countries, barriers to accessing effective CKD therapies must be recognized, and public health strategies must be developed to overcome these obstacles, including training and support at the primary care level to identify individuals at risk of CKD, and appropriately implement clinical practice guidelines.
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.
How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".