The interplay between heart failure and chronic kidney disease
Bibliographic record
Abstract
Chronic kidney disease (CKD) and heart failure (HF) are two globally prevalent, independent, long-term conditions, which often coexist in an individual and display a bidirectional yet interconnected relationship. The presence of CKD often leads to the development of HF and vice versa, which propagates the worsening of each disease, reflecting an intertwined disease cycle. Both HF and CKD share common risk factors, such as increasing age, diabetes, high blood pressure, obesity and smoking. Data show that approximately half of all people with HF also have CKD, which impacts patient burden and quality of life due to a significantly greater risk of hospitalization and death, compared with those that have either CKD or HF. To maximize treatment effectiveness in individuals with both HF and CKD, healthcare professionals should recognize that these two diseases are systemic conditions, representing organ-specific manifestations of similar underlying processes. It is also essential to understand the role of renin-angiotensin system inhibitors, sodium-glucose cotransporter 2 inhibitors, the nonsteroidal mineralocorticoid receptor antagonist finerenone, and glucagon-like peptide-1 receptor agonists in managing these conditions. Lifestyle modifications should also be recommended. This review discusses factors contributing to the interplay between HF and CKD and the key role of healthcare professionals in providing appropriate treatment for the co-existing diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".