Disorders of potassium homeostasis after kidney transplantation
Bibliographic record
Abstract
Disturbances of potassium balance are often encountered when managing kidney transplant recipients (KTR). Both hyperkalemia and hypokalemia may present either as medical emergencies or chronic outpatient abnormalities. Despite the high incidence of hyperkalemia and its potential life-threatening implications, consensus on its management in KTR is lacking. Hypokalemia in KTR is also well-described, although it is given less attention by clinicians compared to hyperkalemia. This article discusses the etiology, pathophysiology and management of both types of potassium disorders in KTR. Once any emergent situation has been corrected, treatment approaches include correcting insulin deficiency if present, adjusting non-immunosuppressive and immunosuppressive medications, eliminating or supplementing potassium as needed, and dietary counselling. Although commonly of multifactorial etiology, ascertaining the specific cause in a particular patient will help guide successful management. Monitoring KTR through regular laboratory testing is essential to detect serious disturbances in potassium balance since patients are often asymptomatic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".