Peritoneal Protein Clearance and not Peritoneal Membrane Transport Status Predicts Survival in a Contemporary Cohort of Peritoneal Dialysis Patients
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Fast peritoneal membrane transport status may be due to inflammation or increased peritoneal membrane surface area. We evaluated the ability of peritoneal protein clearance (Pcl) to distinguish fast peritoneal membrane transport status as a consequence of peritoneal membrane inflammation and assess its impact on patient survival. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Patients who initiated peritoneal dialysis at our center since January 1998 and had a baseline peritoneal equilibration test, measurement of dialysis adequacy, and 24-h dialysate Pcl were included. Demography, comorbidities, and biochemical data were prospectively collected. Follow-up was until death or the end of the period studied. Multivariate regression analysis identified factors that were associated with Pcl. A Cox proportional hazards model was used to identify factors that were associated with survival. RESULTS: A total of 192 patients (56% men, mean age 54.3 +/- 15.3; 32% with diabetes) were included. On univariate analysis, Pcl was negatively correlated with serum albumin and positively correlated with age, dialysate/plasma creatinine ratio (D/Pcr), the presence of peripheral vascular disease, and urine volume. On multivariate analysis, serum albumin, D/Pcr, urine volume, and peripheral vascular disease remained significant. Predictors of mortality were age, comorbidity grade, and Pcl but not D/Pcr. CONCLUSIONS: In this cohort, peritoneal transport status no longer predicted survival, whereas Pcl remained a predictor. Increased large-pore protein loss may reflect the severity of underlying cardiovascular disease, portending a poor prognosis for these patients.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".