Encapsulating peritoneal sclerosis: Importance to the hemodialysis practitioner
Bibliographic record
Abstract
Encapsulating peritoneal sclerosis (EPS) is a rare but devastating complication of long-term peritoneal dialysis (PD) therapy. Encapsulating peritoneal sclerosis is characterized by peritoneal membrane inflammation, followed by progressive peritoneal membrane fibrosis and intestinal encapsulation. Clinical manifestations include ascites as well as intermittent and recurrent small bowel obstruction. The prognosis of EPS is poor. The exact cause of EPS remains unknown. While the risk factors for EPS are not well elucidated, EPS is seen with increased frequency after an increased duration of PD therapy. In more than half the patients who develop EPS, the diagnosis is made after transfer to hemodialysis (HD). It is important for the HD practitioner to initiate surveillance in any patient at risk for EPS while maintaining a heightened index of suspicion for EPS in an HD patient with gastrointestinal symptoms and a history of previous PD therapy. Early diagnosis and prompt initiation of treatment is essential. Early in the course of EPS, immunosuppressive therapy remains the mainstay of treatment. Ultimately, parenteral nutritional support may be required along with surgical therapy to relieve intestinal obstruction. We report a case of EPS in an HD patient at our center highlighting the incidence, risk factors, and treatment strategies in the context of available evidence.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".