Continuous Kidney Replacement Therapies: Core Curriculum 2025
Bibliographic record
Abstract
Critically ill patients that require kidney replacement therapy (KRT) are among the most ill and complex patients routinely encountered in the intensive care unit (ICU). Continuous KRT (CKRT) is used across many ICUs as the therapy of choice for hemodynamically unstable patients with kidney failure. Though existing trials have not shown superior survival or kidney recovery with CKRT relative to intermittent KRT, CKRT has largely become the standard of care in developed nations for the treatment of acute kidney injury (AKI) in patients with shock, acute brain injury, acute liver failure, and other forms of critical illness. As health care systems provide an ever-widening scope of organ-support therapies to increasingly complicated ICU patients, the use of CKRT is likely to expand. In this Core Curriculum, we review the physicochemical principles of CKRT, provide a comprehensive yet practical review of when and how to prescribe CKRT, and summarize seminal trials that serve as the foundations for our approaches to timing of initiation, dosing, vascular access, and anticoagulation for CKRT. We conclude by briefly highlighting a variety of essential, yet often underappreciated, components of the provision of high-value multidisciplinary care to patients receiving CKRT, including drug dosing, nutrition, physical rehabilitation, and CKRT quality assurance programs.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".