The Association between Exit Site Infection and Subsequent Peritonitis among Peritoneal Dialysis Patients
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Peritonitis is the most common infectious complication seen in peritoneal dialysis (PD). Traditionally, exit site infection (ESI) has been thought to predispose PD patients to peritonitis, although the risks have not been quantified. This study aimed to quantify the risk of PD peritonitis after ESI. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Data from 203 clinically stable PD patients >18 years of age who were followed as part of a randomized controlled trial over 18 months were used to estimate the risk of developing peritonitis within 30 days of an ESI compared with individuals who did not have a recent ESI. Sensitivity analyses were performed at 15, 45, and 60 days. RESULTS: Patients were mostly male (64.5%) and Caucasian, with a mean age of 60.5 ± 14.4 years. There were 44 ESIs and 87 peritonitis episodes during the 18-month study. Seven patients had an ESI followed by peritonitis within 30 days. Using a frailty model, patients who had an ESI had a significantly higher risk of developing peritonitis within 30 days, even if the ESI was appropriately treated. This risk was maximal early on and diminished with time, with hazard ratios (95% confidence interval) of 11.1 at 15 days (HR=11.1, 95% CI=4.9-25.1), 5.3 at 45 days (2.5-11.3), and 4.9 at 60 days (2.4-9.9). In 2.3% of patients, subsequent peritonitis was caused by the same organism as the previous ESI. CONCLUSIONS: A strong association between a treated ESI and subsequent PD peritonitis was present up to 60 days after initial diagnosis.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".