Peritonitis and Exit Site Infections in First Nations Patients on Peritoneal Dialysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: First Nations (FN) patients on peritoneal dialysis experience poor outcomes. Whether discrepancies exist regarding the microbiology, rate of infections, and outcomes between FN and non-FN peoples remains unknown. Design, setting, participants, & measures: All adult peritoneal dialysis patients (n = 727) from 1997 to 2007 residing in Manitoba, Canada, were included. Parametric and nonparametric tests were used as necessary. Negative binomial regression was used to determine the relationship of rates of exit site infections (ESIs) and peritonitis between FN and non-FN peoples. RESULTS: A total of 161 FN and 566 non-FN subjects were included in the analyses. The unadjusted relative rates of peritonitis and ESIs in FN subjects were 132.7 and 86.0/100 patient-years compared with 87.8 and 78.2/100 patient-years in non-FN populations, respectively. FN subjects were more likely to have culture-negative peritonitis (36.5 versus 20.8%, P < 0.0001) and Staphylococcus ESIs (54.1 versus 32.9%, P < 0.0001). The crude and adjusted rates of peritonitis were higher in FN subjects for total episodes and culture-negative and gram-negative peritonitis. Catheter removal because of peritonitis was similar in both groups (42.9 versus 38.1% for FN and non-FN subjects, respectively; P = 0.261). CONCLUSIONS: FN patients experience higher rates of peritonitis and similar rates of ESIs compared with non-FN patients. Interventions to improve outcomes and prevent infections should specifically be targeted to the FN population.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".