Preventing acute kidney injury and its longer-term impact in the critically ill
Bibliographic record
Abstract
Acute kidney injury (AKI) is a heterogeneous syndrome that not only affects short-term morbidity and mortality but also influences long-term outcomes. AKI is part of acute kidney disease (AKD) that encompasses a range of different conditions and is characterized by a kidney dysfunction lasting 90 days or less after which time the term chronic kidney disease (CKD) applies. AKD may result in irreversible loss of nephrons and may lead to CKD. In this narrative review, an update on different aspects of AKI in critically ill patients will be provided. We discuss biomarkers for early diagnosis of AKI, sub-clinical AKI, as well as AKI-AKD-CKD transition. In addition, various strategies to prevent the development of AKI, including the application of amino acids, remote-ischemic preconditioning, hemoadsorption, and a kidney prevention strategy, will be discussed. Finally, the choice of adequate endpoints for AKI prevention trials will be addressed."Take home message".AKI and even subclinical AKI impact short- and long-term outcome and therefore, prevention of kidney injury is of utmost importance. As several strategies have been proven to be effective in preventing the development of AKI, these therapies should be implemented in daily practice to improve patient outcomes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".