Peritoneal Dialysis in Critically Ill Patients
Bibliographic record
Abstract
Peritoneal dialysis (PD) as an AKI treatment in adults was widely accepted in critical care settings well into the 1980s. The advent of extracorporeal continuous KRT led to widespread decline in the use of PD for AKI across high-income countries. The lack of familiarity and comfort with the use of PD in critical care settings has also led to lack of use even among those receiving maintenance PD. Many critical care units reflexively convert patients receiving maintenance PD to alternative dialysis therapies at admission. Renewed interest in the use of PD for AKI therapy has emerged due to its increasing use in low- and middle-income countries. In high-income countries, the coronavirus disease 2019 (COVID-19) pandemic, saw PD for AKI used early on, where many critical care units were in crisis and relied on PD use when resources for other AKI therapy modalities were limited. In this review, we highlight advantages and disadvantages of PD in critical care settings and indications and contraindications for its use. We provide an overview of literature to support both PD treatment during AKI and its continuation as a maintenance therapy during critical illness. For AKI therapy, we further discuss establishment of PD access, PD prescription management, and complication monitoring and treatment. Finally, we discuss expansion in the use of PD for AKI therapy extending beyond its role during times of resource constraints.
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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".