Independent Effects of Systemic and Peritoneal Inflammation on Peritoneal Dialysis Survival
Bibliographic record
Abstract
Systemic inflammation, as evidenced by elevated inflammatory cytokines, is a feature of advanced renal failure and predicts worse survival. Dialysate IL-6 concentrations associate with variability in peritoneal small solute transport rate (PSTR), which has also been linked to patient survival. Here, we determined the link between systemic and intraperitoneal inflammation with regards to peritoneal membrane function and patient survival as part of the Global Fluid Study, a multinational, multicenter, prospective, combined incident and prevalent cohort study (n=959 patients) with up to 8 years of follow-up. Data collected included patient demographic characteristics, comorbidity, modality, dialysis prescription, and peritoneal membrane function. Dialysate and plasma cytokines were measured by electrochemiluminescence. A total of 426 survival endpoints occurred in 559 incident and 358 prevalent patients from 10 centers in Korea, Canada, and the United Kingdom. On patient entry to the study, systemic and intraperitoneal cytokine networks were dissociated, with evidence of local cytokine production within the peritoneum. After adjustment for multiple covariates, systemic inflammation was associated with age and comorbidity and independently predicted patient survival in both incident and prevalent cohorts. In contrast, intraperitoneal inflammation was the most important determinant of PSTR but did not affect survival. In prevalent patients, the relationship between local inflammation and membrane function persisted but did not account for an increased mortality associated with faster PSTR. These data suggest that systemic and local intraperitoneal inflammation reflect distinct processes and consequences in patients treated with peritoneal dialysis, so their prevention may require different therapeutic approaches; the significance of intraperitoneal inflammation requires further elucidation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 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".