Bibliographic record
Abstract
PURPOSE OF REVIEW: Although renin-angiotensin aldosterone system (RAAS) inhibitors have become the mainstay treatment for patients with chronic diseases, hyperkalemia is a major contributory deterrent to their use in patients with chronic kidney disease (CKD) and heart failure. For the first time in 50 years, two new therapies (patiromer and ZS-9) have recently emerged for the concomitant treatment of hyperkalemia in these patients. The objective of this review is to discuss the efficacy and safety of these new agents. RECENT FINDINGS: Patiromer effectively reduces serum potassium in patients with CKD and heart failure, even with the concomitant use of RAAS inhibitors. The most common adverse events in clinical trials were gastrointestinal events. ZS-9 (Lokelma) rapidly reduces serum potassium levels and to a greater magnitude, and has a role in the acute management of hyperkalemia. Despite having more adverse events than patiromer, ZS-9 is overall well tolerated. SUMMARY: These new therapies show promising results for the chronic management of hyperkalemia, whilst also potentially allowing for the concomitant use of RAAS inhibitors at optimal doses. More research is needed to examine the benefits of continuation of RAAS inhibitors after an episode of hyperkalemia in patients with CKD and heart failure.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".