Histopathological Changes of Long-Term Peritoneal Dialysis Using Physiological Solutions: A Case Report and Review of the Literature
Bibliographic record
Abstract
BACKGROUND: Long-term peritoneal dialysis (PD), especially with nonphysiological solutions, is afflicted with the severe complication of encapsulating peritoneal sclerosis (EPS). Physiologic PD solutions have been introduced to reduce pH trauma. Data on peritoneal biopsies in pediatrics with long-term PD using physiological solutions are scant. CASE REPORT: We report an adolescent who had been on 10-h continuous hourly cycles using mostly 2.27% Physioneal™ for 5 years. There were two episodes of peritonitis in October 2017 (Klebsiella oxytoca) and May 2018 (Klebsiella pneumoniae), which were treated promptly. This adolescent, who lost two kidney transplants from recurrent focal and segmental glomerulosclerosis, underwent a peritoneal membrane biopsy at the time of a third PD catheter placement, 16 months after the second renal transplant. Laparoscopically, the peritoneum appeared grossly normal, but fibrosis and abundant hemosiderin deposition were noted on histology. The thickness of the peritoneum was 200-900 (mean 680) µm; normal for age of 14 years is 297 [IQR 229, 384] μm. The peritoneum biopsy did not show specific EPS findings, as the mesothelial cells were intact, and there was a lack of fibrin exudation, neo-membrane, fibroblast proliferation, infiltration, or calcification. CONCLUSIONS: While the biopsy was reassuring with respect to the absence of EPS, significant histopathological changes suggest that avoiding pH trauma may not ameliorate the effects of glucose exposure in long-term PD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".