More signals that overuse of healthcare is a pervasive problem contributing to health system waste
Bibliographic record
Abstract
In Australia, approximately 1.7 million adults have evidence of chronic kidney disease (CKD).This complex disease can result in a multitude of complications, including hyperkalaemia, which is common and well recognised.The advent of new therapeutics aimed at lowering serum potassium has raised the possibility of optimising potassium control to enable greater use of renin-angiotensin-aldosterone system inhibitors in the management of CKD.Recent studies suggest that hyperkalaemia also has implications for peripheral neuropathy in CKD, a complication that substantially contributes to patient morbidity.This review examines evidence of the relationship between potassium and peripheral neuropathy, with a discussion of clinical implications.We searched PubMed for original and review articles using pre-specified key words, clinical guidelines and population data.The major findings were that contemporary CKD cohorts demonstrate a high prevalence of peripheral neuropathy, even in stage 3-4 CKD, including those without diabetes.The severity of the problem has been emphasised by an ominous rise in foot complications and amputation rates in dialysis patients, highlighting the need for increased awareness of the condition in earlier stages of CKD and targeted treatment strategies.It is likely that the pathophysiology of peripheral neuropathy in CKD is multifaceted, with potential influences from potassium, vascular abnormalities, diabetes, inflammation and unknown middle molecules.Despite these complexities, the relationship between potassium and nerve function in dialysis has been well established, and recent research in stage 3-4 CKD suggests that assertive potassium control may improve neuromuscular outcomes in CKD.These small studies should be confirmed in large, multicentre settings.
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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 | 0.040 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".