Timing of Renal Replacement Therapy in Acute Kidney Injury
Bibliographic record
Abstract
BACKGROUND: Renal replacement therapy (RRT) is commonly and increasingly utilized in critically ill patients with severe acute kidney injury (AKI). The issue of when to start RRT in a critically ill patient with AKI has long troubled clinicians. SUMMARY: Currently, there is a paucity of high-quality evidence to guide clinician decision-making on the optimal time to start RRT. This lack of evidence has translated into wide variation in treatment patterns and practices. In patients developing life-threatening complications of AKI, the decision to start RRT is largely indisputable; however, in the absence of such complications, the optimal thresholds to start RRT that translates into improved outcomes for patients are unknown. Available evidence from observational studies and clinical trials have considerable limitations for translation to clinical practice due to their retrospective, post hoc secondary design, their small sample sizes, heterogeneity in study populations and illness severity, variation in the definitions of AKI and in the timing of or thresholds for starting RRT and the risk of residual confounding and bias related to the association between the timing of RRT and outcome. KEY MESSAGES: Several large randomized trials are planned or ongoing, and the results of these trials will greatly inform best clinical practice and will help reduce unnecessary variation in the practice of RRT prescription. For now, the decision on the appropriate time to start RRT is naturally complex, integrating numerous variables, and should largely be individualized.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".