Frequency and Microbiology of Peritonitis and Exit-Site Infection among Obese Peritoneal Dialysis Patients
Bibliographic record
Abstract
BACKGROUND: Data on obesity as a risk factor for peritonitis and catheter infections among peritoneal dialysis (PD) patients are limited. Furthermore, little is known about the microbiology of PD-related infections among patients with a high body mass index (BMI). METHODS: Using a cohort that included all adult patients residing in the province of Manitoba who received PD during the period 1997 - 2007, we studied the relationship between BMI and PD-related infections. After categorizing patients into quartiles of BMI, a multivariate Cox regression model was used to determine the independent relationship between BMI and peritonitis or exit-site infection (ESI). We also studied whether increasing BMI was associated with a propensity to infections with particular organisms. RESULTS: Among 990 PD patients, 938 (95%) had accurate BMI data available. Those 938 patients experienced 1338 peritonitis episodes and 1194 exit-site infections. In unadjusted analyses, patients in the highest BMI quartile (median: 33.5; interquartile range: 31.9 - 36.4) had an increased risk of peritonitis overall, and also an increased risk of peritonitis with gram-positive organisms and coagulase-negative Staphylococcus (CNS). After multivariate adjustment for age, sex, diabetes, cause of renal disease, Aboriginal race, PD modality, and S. aureus nasal carriage, the relationship between overall peritonitis risk and BMI disappeared, but the increased risk of CNS peritonitis among patients in the highest BMI quartile persisted (hazard ratio: 1.80; 95% confidence interval: 1.06 to 3.06; p = 0.03). There was no increased risk of ESI among patients in the highest BMI quartile on univariate analysis or after multivariate adjustment. CONCLUSIONS: Among Canadian PD patients, obesity was not associated with an increased risk of peritonitis overall, but may be associated with a higher risk of CNS peritonitis.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".