The risk of peritonitis after an exit site infection: a time-matched, case–control study
Bibliographic record
Abstract
BACKGROUND: Exit site infections (ESIs) have been previously associated with the development of peritonitis; however, the evidence to support this association is limited. We conducted a time-matched, case-control study to determine the association between ESIs and subsequent peritonitis. METHODS: The cohort comprised 962 incident adult peritoneal dialysis (PD) patients from January 2000 to December 2009. Patients with an ESI were matched to those with no ESI based on the duration of PD. The subsequent risk of peritonitis was determined using Cox models and conditional logistic regression. RESULTS: During the study period, there were a total of 1002 ESI and 1228 peritonitis episodes among 962 individuals. The time to subsequent peritonitis was shorter in individuals who had at least one ESI [hazard ratio (HR) 1.59; 95% confidence interval (CI) 1.22-2.07, P<0.001]. The risk of peritonitis post-ESI was increased for all Gram-positive infections [adjusted hazard ratio (aHR) 1.75; 95% CI 1.25-2.43], and for the subtypes of coagulase-negative Staphylococcus (CNS) and S. aureus, but not for Gram-negative or culture-negative infections. These findings were similar when examining the odds of subsequent peritonitis within prespecified time intervals of the ESI through conditional logistic regression. CONCLUSIONS: The risk of peritonitis after ESI is increased, particularly with S. aureus and CNS, despite appropriate antibiotic treatment of the ESI.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".