Center-Specific Factors Associated with Peritonitis Risk—A Multi-Center Registry Analysis
Bibliographic record
Abstract
UNLABELLED: ♦ BACKGROUND: Previous studies have reported significant variation in peritonitis rates across dialysis centers. Limited evidence is available to explain this variability. The aim of this study was to assess center-level predictors of peritonitis and their relationship with peritonitis rate variations. ♦ METHODS: All incident peritoneal dialysis (PD) patients treated in Australia between October 2003 and December 2013 were included. Data were accessed through the Australia and New Zealand Dialysis and Transplant Registry. The primary outcome was peritonitis rate, evaluated in a mixed effects negative binomial regression model. Peritonitis-free survival was assessed as a secondary outcome in a Cox proportional hazards model. ♦ RESULTS: Overall, 8,711 incident PD patients from 51 dialysis centers were included in the study. Center-level predictors of lower peritonitis rates included smaller center size, high proportion of PD, low peritoneal equilibration test use at PD start, and low proportion of hospitalization for peritonitis. In contrast, a low proportion of automated PD exposure, high icodextrin exposure and low or high use of antifungal prophylaxis at the time of peritonitis were associated with a higher peritonitis rate. Similar results were obtained for peritonitis-free survival. Overall, accounting for center-level characteristics appreciably decreased peritonitis variability among dialysis centers (p = 0.02). ♦ CONCLUSION: This study identified specific center-level characteristics associated with the variation in peritonitis risk. Whether these factors are directly related to peritonitis risk or surrogate markers for other center characteristics is uncertain and should be validated in further studies.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".