Bibliographic record
Abstract
PURPOSE OF REVIEW: Hyperkalemia is increasingly prevalent in the heart failure population as more people live with heart failure and comorbid conditions such as diabetes and chronic kidney disease. Furthermore, renin-angiotensin-aldosterone (RAAS) inhibitors are a key component of clinical therapy in these populations. Until now, we have not had any reliable or tolerable therapies for treatment of hyperkalemia resulting in inability to implement or achieve target doses of RAAS inhibition. This review will focus on two new therapies for hyperkalemia: patiromer and sodium zirconium cyclosilicate (SZC). RECENT FINDINGS: Patiromer and SZC have been studied in heart failure and both agents have demonstrated the ability to maintain normokalemia for extended periods of time with improved side effect profiles than existing potassium binders such as sodium polystyrene sulfate, though no direct comparisons have occurred. SZC has also shown promise in the treatment of acute hyperkalemia with its quick onset of action. SUMMARY: Patiromer and SZC will be useful adjuncts in the clinical care of heart failure patients with hyperkalemia. These agents will allow clinicians to maintain patients on RAAS inhibitors and uptitrate their guideline directed medical therapy to target doses without the additional concern for recurrent hyperkalemia and its untoward effects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.006 | 0.002 |
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".