Fluid Balance in Patients with Acute Kidney Injury: Emerging Concepts
Bibliographic record
Abstract
Intensive care unit and surgical populations are at increased risk for acute kidney injury (AKI) and oliguria, which often lead to fluid accumulation. Volume resuscitation is a cornerstone in the treatment of hemodynamic instability in these populations. However, fluid balance evaluation and its management in the critically ill can be challenging. Several clinical and paraclinical tools may aid decision-making regarding fluid management. When fluid therapy is indicated, crystalloids should be the preferred agents. Synthetic colloids have been associated with no survival benefit and increased risk of AKI. There is currently a paradigm shift in which hypervolemia is no longer desirable and is increasingly shown to be detrimental to both renal outcomes and survival. Instead, approaches that aim for neutral and slightly negative fluid balance or 'dry' patients after initial fluid resuscitation are favored. This may be achieved by conservative fluid strategies, diuretics or renal replacement therapy. In this paper, we will review recent findings on the principles of fluid management in AKI, including assessment of fluid need, choice of fluid solutions, influence of fluid overload on outcomes, and some practical issues to achieve fluid balance and minimize complications in patients with AKI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".