Therapeutic update on oral potassium exchange resin use in chronic kidney disease patients: a systematic review of randomized controlled clinical trials
Bibliographic record
Abstract
Hyperkalemia is a common electrolyte disorder in patients with chronic kidney disease (CKD) that increases in prevalence with the decline of glomerular fltration rate (GFR). Another risk of hyperkalemia is the use of renin-angiotensin-aldosterone system inhibitors (RAASi) and/or mineralocorticoid receptor antagonists (MRAs) in managing CKD and proteinuria. The treatment of chronic hyperkalemia is challenging especially for outpatients. Treatment options for hyperkalemia include the potassium exchange resins of which two new potassium binders, Patiromer Sorbitex Calcium, and Sodium Zirconium Cyclosilicate (SZC) have demonstrated their clinical efficacy in reducing serum potassium with a positive safety profile. The old potassium exchange resin sodium polystyrene sulfonate (Kayexalate™) has some negative side effects including colonic necrosis, hypomagnesemia, and hypernatremia. In this review and literature search, we compare the available oral potassium exchange resins, highlight their advantages and disadvantages and comment on efficacy and safety parameters specifically in CKD patients.
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 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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".