Potassium homeostasis and management of dyskalemia in kidney diseases: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference
Bibliographic record
Abstract
Potassium disorders are common in patients with kidney disease, particularly in patients with tubular disorders and low glomerular filtration rate. A multidisciplinary group of researchers and clinicians met in October 2018 to identify evidence and address controversies in potassium management. The issues discussed encompassed our latest understanding of the regulation of tubular potassium excretion in health and disease; the relationship of potassium intake to cardiovascular and kidney outcomes, with increasing evidence showing beneficial associations with plant-based diet and data to suggest a paradigm shift from the idea of dietary restriction toward fostering patterns of eating that are associated with better outcomes; the paucity of data on the effect of dietary modification in restoring abnormal serum potassium to the normal range; a novel diagnostic algorithm for hypokalemia that takes into account the ascendency of the clinical context in determining cause, aligning the educational strategy with a practical approach to diagnosis; and therapeutic approaches in managing hyperkalemia when chronic and in the emergency or hospital ward. In sum, we provide here our conference deliberations on potassium homeostasis in health and disease, guidance for evaluation and management of dyskalemias in the context of kidney diseases, and research priorities in each of the above areas.
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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 | 0.032 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.018 |
| 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".