Fluid management in chronic kidney disease: what is too much, what is the distribution, and how to manage fluid overload in patients with chronic kidney disease?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Assessment of fluid status to reach normovolemia in patients with chronic kidney disease (CKD) continues to be a tough task. Besides clinical observation, technological methods have been introduced, yet, the best approach is still uncertain. The present review looks at fluid overload in CKD from three perspectives: the critical fluid threshold leading to adverse cardiovascular outcomes, fluid distribution and its clinical correlates, and direct effect of fluid overload on vascular function related to disturbance of the sodium-skin axis and endothelial glycocalyx dysfunction. RECENT FINDINGS: To determine fluid status, both the absolute and relative fluid overload is used as parameter in clinical practice. In addition, the definition of fluid overload is ambivalent and its relation to symptom burden has not been studied well. Studies on the impact of distribution of fluid are scarce and the limited evidence suggests differences based on the cause of CKD. So far, no standardized technologies are available to adequately determine fluid distribution. After discovering the 'third compartment' of total body sodium in skin and muscle tissue and its potential direct effect on vascular function, other biomarkers such as VEGF-C are promising. SUMMARY: We propose a multimodal clinical approach for volume management in CKD. Because there are currently no studies are available demonstrating that correction of fluid overload in CKD will lead to better outcome, these are strongly needed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".