Biocompatible Solutions and Long-Term Changes in Peritoneal Solute Transport
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The inflammation-driven increase in peritoneal solute transport rate that occurs during long-term peritoneal dialysis is associated with higher mortality, hospitalization, and encapsulating peritoneal sclerosis. Because biocompatible solutions were developed to mitigate these effects, we examined the association with their use and longitudinal peritoneal solute transport rate. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We analyzed subjects from the multinational prospective Global Fluid Study with three or more peritoneal solute transport rate measurements >2 months from the start of peritoneal dialysis. Follow-up was for 7.5 years (median, 2.3 years; interquartile range, 1.8-3.6) in biocompatible solutions and 12.8 years (median, 3.2 years; interquartile range, 1.9-4.3) for standard solutions. Using a random intercept/slopes multilevel model, we examined the association of patients using biocompatible solutions and peritoneal solute transport rate over time, adjusting for center effects, dialysate dextrose concentration, baseline dialysate IL-6 concentration, icodextrin use, residual kidney function, and peritonitis. RESULTS: =0.02). With duration of treatment, there was a continuous increase in peritoneal solute transport rate in patients using standard solutions (range, 2 months to 4 years). In contrast, patients using biocompatible solutions had peritoneal solute transport rates that plateaued after 2 years of therapy. These changes in peritoneal solute transport rate were independent of baseline inflammation and time-varying predictors of faster peritoneal solute transport rate. In patients suffering episodes of peritonitis while using standard solutions, there was an associated increase in peritoneal solute transport rate of 0.020 (95% confidence interval, 0.01 to 0.03) per episode, whereas in patients using biocompatible solutions, there was no change in this parameter (-0.014; 95% confidence interval, -0.03 to <0.01). CONCLUSIONS: These data suggest that a different temporal pattern in changes in peritoneal solute transport rate occurs during the course of peritoneal dialysis according to solution type and that patients using biocompatible solutions may avoid the increase in solute transport associated with peritonitis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".