Chronic kidney disease screening to reduce cardiovascular risk: a call to action
Bibliographic record
Abstract
Chronic kidney disease (CKD) has a high global prevalence, affecting around 1 in 7 adults in the United States; however, most adults with CKD are unaware that they have the condition. Diagnosis and treatment of CKD is essential due to the associated increased morbidity and mortality, including increased risk of cardiovascular disease (CVD) and heart failure. Importantly, people with CKD are more likely to die from CVD than progress to end-stage kidney disease. Dual evaluation of estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio (UACR) is essential to determine the level of risk and to guide appropriate treatment. Although abnormalities in both eGFR and UACR can be modifiable risk factors for CKD progression and adverse CV outcomes, there is evidence of underuse of this dual screening for CKD. However, for patients with diagnosed CKD, striking reductions in cardiorenal risk may be achieved by combining appropriate evidence-based therapies. Current approaches to management of CKD involve the use of multiple therapies that target different pathological pathways to reduce cardiorenal risk. Therefore, we raise a call to action to improve the standard of care for early diagnosis and management of CKD, to minimize the risk of disease progression and complications, reduce CV risk, and ultimately improve patient outcomes. Alongside primary care clinicians, cardiologists can also lead the way for preventive efforts and implementation of guideline-directed therapies that can reduce the risk of both CKD progression and adverse CV outcomes.
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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 | 0.031 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.033 | 0.043 |
| Insufficient payload (model declined to judge) | 0.027 | 0.010 |
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".