New Insights on Intravenous Fluids, Diuretics and Acute Kidney Injury
Bibliographic record
Abstract
Acute kidney injury (AKI) is commonly and increasingly encountered in patients with critical illness. Fluid therapy is the cornerstone for the prevention and management of critically ill patients with AKI. New data have emerged that have raised concern that specific types of fluid (i.e. hydroxyethylstarch) may either contribute to or exacerbate AKI. Additional data have accumulated to indicate that the unnecessary accumulation of fluid and volume overload can negatively impact clinical outcomes. This finding may be further compounded in patients with oliguric AKI where solute and free water elimination are impaired. Diuretic therapy in AKI remains controversial. However, diuretic use is common, despite a paucity of evidence to show improved clinical outcomes. There are few therapeutic interventions proven to impact the clinical course and outcome of critically ill patients with established AKI. Current management strategies center largely on supportive care, with rapid resuscitation, removal of the stimulus contributing to AKI, judicious avoidance of complications, and allowing time for recovery. In this review, we explore recent insights on intravenous fluid therapy, volume overload, and diuretic therapy in the context of the critically ill 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 | 0.004 |
| 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) | 0.003 | 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".