Optimizing Peritoneal Dialysis–Associated Peritonitis Prevention in the United States
Bibliographic record
Abstract
Peritoneal dialysis (PD)-associated peritonitis is the leading cause of permanent transition to hemodialysis among patients receiving PD. Peritonitis is associated with higher mortality risk and added treatment costs and limits more widespread PD utilization. Optimizing the prevention of peritonitis in the United States will first require standardization of peritonitis definitions, key data elements, and outcomes in an effort to facilitate nationwide reporting. Standardized reporting can also help describe the variability in peritonitis rates and outcomes across facilities in the United States in an effort to identify potential peritonitis prevention strategies and engage with stakeholders to develop strategies for their implementation. Here, we will highlight considerations and challenges in developing standardized definitions and implementation of national reporting of peritonitis rates by PD facilities. We will describe existing peritonitis prevention evidence gaps, highlight successful infection-reporting initiatives among patients receiving in-center hemodialysis or PD, and provide an overview of nationwide quality improvement initiatives, both in the United States and elsewhere, that have translated into a reduction in peritonitis incidence. We will discuss opportunities for collaboration and expansion of the Nephrologists Transforming Dialysis Safety (NTDS) initiative to develop knowledge translation pathways that will lead to dissemination of best practices in an effort to reduce peritonitis incidence.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".