Variability in Culture-Negative Peritonitis Rates in Pediatric Peritoneal Dialysis Programs in the United States
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: International guidelines suggest a target culture-negative peritonitis rate of <15% among patients receiving long-term peritoneal dialysis. Through a pediatric multicenter dialysis collaborative, we identified variable rates of culture-negative peritonitis among participating centers. We sought to evaluate whether specific practices are associated with the variability in culture-negative rates between low- and high-culture-negative rate centers. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Thirty-two pediatric dialysis centers within the Standardizing Care to Improve Outcomes in Pediatric End Stage Renal Disease (SCOPE) collaborative contributed prospective peritonitis data between October 1, 2011 and March 30, 2017. Clinical practice and patient characteristics were compared between centers with a ≤20% rate of culture-negative peritonitis (low-rate centers) and centers with a rate >20% (high-rate centers). In addition, centers completed a survey focused on center-specific peritoneal dialysis effluent culture techniques. RESULTS: <0.001). The survey demonstrated that peritoneal dialysis effluent culture techniques were highly variable across centers. No consistent practice or technique helped to differentiate low- and high-rate centers. CONCLUSIONS: Culture-negative peritonitis is a frequent complication of maintenance peritoneal dialysis in children. Despite published recommendations for dialysis effluent collection and culture methods, great variability in culture techniques and procedures exists among individual dialysis programs and respective laboratory processes.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".