Hyperkalemia: Prevalence, Predictors and Emerging Treatments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is well established that an elevated potassium level (hyperkalemia) is associated with a risk of adverse events including morbidity, mortality and healthcare system cost. Hyperkalemia is commonly encountered in many chronic conditions including kidney disease, diabetes and heart failure. Furthermore, hyperkalemia may result from the use of renin-angiotensin-aldosterone system inhibitors (RAASi), which are disease-modifying treatments for these conditions. Therefore, balancing the benefits of optimizing treatment with RAASi while mitigating hyperkalemia is crucial to ensure patients are optimally treated. In this review, we will briefly discuss the definition, causes, epidemiology and consequences of hyperkalemia. The majority of the review will be focused on management of hyperkalemia in the acute and chronic setting, emphasizing contemporary approaches and evolving data on the relevance of dietary restriction and the use of novel potassium binders.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it