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Record W4390012427 · doi:10.3389/jpps.2023.11892

Therapeutic update on oral potassium exchange resin use in chronic kidney disease patients: a systematic review of randomized controlled clinical trials

2023· review· en· W4390012427 on OpenAlexvenueno aff
Jaclyn Gruver, Akram Al‐Makki, Brian Shepler

Bibliographic record

VenueJournal of Pharmacy & Pharmaceutical Sciences · 2023
Typereview
Languageen
FieldMedicine
TopicPotassium and Related Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsHyperkalemiaMedicineKidney diseaseElectrolyte DisorderAldosteroneInternal medicineHypokalemiaEplerenoneSpironolactoneUrologyPharmacologyEndocrinologyHyponatremia

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.069
metaresearch head score (Gemma)0.057
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.286
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0690.057
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0240.008
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.302
GPT teacher head0.541
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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