Acute hyperkalemia in the emergency department: a summary from a Kidney Disease: Improving Global Outcomes conference
Bibliographic record
Abstract
Hyperkalemia is a common electrolyte disorder observed in the emergency department. It is often associated with underlying predisposing conditions, such as moderate or severe kidney disease, heart failure, diabetes mellitus, or significant tissue trauma. Additionally, medications, such as inhibitors of the renin-angiotensin-aldosterone system, potassium-sparing diuretics, nonsteroidal anti-inflammatory drugs, succinylcholine, and digitalis, are associated with hyperkalemia. To this end, Kidney Disease: Improving Global Outcomes (KDIGO) convened a conference in 2018 to identify evidence and address controversies on potassium management in kidney disease. This review summarizes the deliberations and clinical guidance for the evaluation and management of acute hyperkalemia in this setting. The toxic effects of hyperkalemia on the cardiac conduction system are potentially lethal. The ECG is a mainstay in managing hyperkalemia. Membrane stabilization by calcium salts and potassium-shifting agents, such as insulin and salbutamol, is the cornerstone in the acute management of hyperkalemia. However, only dialysis, potassium-binding agents, and loop diuretics remove potassium from the body. Frequent reevaluation of potassium concentrations is recommended to assess treatment success and to monitor for recurrence of hyperkalemia.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".