Renal Replacement Therapy in Adult Critically Ill Patients: When to Begin and When to Stop
Bibliographic record
Abstract
Renal replacement therapy (RRT) is an important therapeutic and supportive measure for acute kidney injury (AKI) in the critical care setting. While RRT is extensively used in clinical practice, there remains uncertainty about the ideal circumstances of when to initiate RRT and for what indications. Many factors, including logistics, resource availability, physician experience and patient-related factors are involved in the decision of when to start and stop RRT for those with AKI. Among the patient-related factors, examples include 'dynamic' trends in AKI and/or non-kidney organ dysfunction, additional measures of acute physiology, such as fluid accumulation and relative oliguria. There currently exists a large variation in clinical practice regarding starting and stopping RRT, due in part to the lack of consensus on this issue. In this article, we briefly review a new opinion-based algorithm to aid in the decision on when to initiate RRT in adult critically ill patients. This algorithm was developed using available clinical evidence, recognizing the inherent limitations of observational studies. It aims to provide a starting point for clinicians and future prospective studies. We also review the available literature on discontinuation of RRT and propose a few simple recommendations on how to 'wean' patients from RRT.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".