Predictors of Peritonitis in Patients on Peritoneal Dialysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Despite the decreasing incidence of peritonitis among peritoneal dialysis (PD) patients over time, its occurrence is still associated with significant morbidity and mortality. Determining factors that are associated with PD peritonitis may facilitate the identification of patients who are at risk. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Using data collected in the multicenter Baxter POET database between 1996 and 2005, the study population included incident Canadian PD patients. Potential predictors of peritonitis were sought using a negative binomial model and an Andersen-Gill model. Study variables included age, gender, race, cause of renal disease, diabetes status, transfer from hemodialysis (HD), previous renal transplant, and continuous ambulatory PD (CAPD) versus automated PD (APD). RESULTS: Data were available for 4247 incident PD patients, including 1605 patients with a total of 2555 peritonitis episodes. Using the negative binomial regression model, factors that were independently associated with a higher peritonitis rate included age, Black race, and having transferred from HD. There was an interaction between gender and diabetes, with an increased risk for peritonitis among female patients with diabetes. The use of CAPD versus APD did not affect the peritonitis rate. The Andersen-Gill model for recurrent events yielded similar results. CONCLUSIONS: Predictors of PD peritonitis included Black race, transferring from HD to PD, and diabetes among women. In contrast to previous findings, CAPD and APD were similar with regard to peritonitis risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".