Timing of renal-replacement therapy in intensive care unit-related acute kidney injury
Bibliographic record
Abstract
PURPOSE OF REVIEW: The optimal timing of renal-replacement therapy (RRT) initiation for the management of acute kidney injury (AKI) in the intensive care unit (ICU) is frequently controversial. An earlier-strategy has biological rationale, even in the absence of urgent indications; however, a delayed-strategy may prevent selected patients from receiving RRT and avoid complications related to RRT. RECENT FINDINGS: Previous studies assessing the optimal timing of RRT initiation found conflicting results, contributing to variation in clinical practice. The recent multinational trial, standard vs. accelerated initiation of renal replacement therapy in acute kidney injury (STARRT-AKI) found no survival benefit and a higher risk of RRT dependence with an accelerated compared to a standard RRT initiation strategy in critically ill patients with severe AKI. Nearly 40% of patients allocated to the standard-strategy group did not receive RRT. The Artificial Kidney Initiation in Kidney Injury-2 (AKIKI-2) trial further assessed delayed compared to more-delayed strategies for RRT initiation. The more-delayed strategy did not confer an increase in RRT-free days and was associated with a higher risk of death. SUMMARY: Early preemptive initiation of RRT in critically ill patients with AKI does not confer clear clinical benefits. However, protracted delays in RRT initiation may be harmful.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".