Microbiology of Peritonitis in Peritoneal Dialysis Patients with Multiple Episodes
Bibliographic record
Abstract
BACKGROUND: Peritoneal dialysis (PD)-associated peritonitis clusters within patients. Patient factors contribute to peritonitis risk, but there is also entrapment of organisms within the biofilm that forms on PD catheters. It is hypothesized that this biofilm may prevent complete eradication of organisms, predisposing to multiple infections with the same organism. METHODS: Using data collected in the Canadian multicenter Baxter POET (Peritonitis, Organism, Exit sites, Tunnel infections) database from 1996 to 2005, we studied incident PD patients with 2 or more peritonitis episodes. We determined the proportion of patients with 2 or more episodes caused by the same organism. In addition, using a multivariate logistic regression model, we tested whether prior peritonitis with a given organism predicted the occurrence of a subsequent episode with the same organism. RESULTS: During their time on PD, 558 patients experienced 2 or more peritonitis episodes. Of those 558 patients, 181 (32%) had at least 2 episodes with the same organism. The organism most commonly causing repeat infection was coagulase-negative Staphylococcus (CNS), accounting for 65.7% of cases. Compared with peritonitis caused by other organisms, a first CNS peritonitis episode was associated with an increased risk of subsequent CNS peritonitis within 1 year (odds ratio: 2.1; 95% confidence interval: 1.5 to 2.8; p < 0.001). Among patients with repeat CNS peritonitis, 48% of repeat episodes occurred within 6 months of the earlier episode. CONCLUSIONS: In contrast to previous data, we did not find a high proportion of patients with multiple peritonitis episodes caused by the same organism. Coagulase-negative Staphylococcus was the organism most likely to cause peritonitis more than once in a given patient, and a prior CNS peritonitis was associated with an increased risk of CNS peritonitis within the subsequent year.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".