Core Interventions for the Prevention of Peritoneal Dialysis–Related Infections
Bibliographic record
Abstract
For some patients, peritoneal dialysis (PD) has several advantages over center-based hemodialysis. PD-related infections such as peritonitis and PD catheter exit site and tunnel infections are a significant source of morbidity. Peritonitis leads to increased mortality, and it is the leading cause of transfer off PD. Infection prevention practices may vary widely across dialysis centers, resulting in significant differences in infection rates. To address these issues and improve patient outcomes, the American Society of Nephrology facilitated the development of core interventions aimed at reducing PD-related infections across US dialysis facilities. The core interventions focus on six key strategies: ( 1 ) regular surveillance and feedback on infection rates, ( 2 ) standardized staff training and competency assessments, ( 3 ) standardized patient and care partner/caregiver education, ( 4 ) routine infection prevention assessments, ( 5 ) antimicrobial prophylaxis for PD catheter exit sites, and ( 6 ) prophylactic antimicrobials for certain procedures and events. These strategies, on the basis of evidence and international guidelines, emphasize consistency in implementation and monitoring at the facility level. The workgroup followed an iterative process, incorporating expert review and feedback to inform and refine these interventions. Effective implementation requires coordinated efforts among dialysis teams, patients, and support networks, with ongoing evaluation through surveillance and quality improvement initiatives. Although the interventions are grounded in current evidence, additional research is necessary to refine practices and address emerging challenges. The goal is to reduce infection risks, improve the quality of life for patients on PD, and support national efforts to expand home dialysis use.
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".