Bibliographic record
Abstract
PURPOSE OF REVIEW: To clarify misconceptions about the feasibility and risks of peritoneal dialysis that unnecessarily limit peritoneal dialysis uptake or continuation in patients for whom peritoneal dialysis is the preferred dialysis modality. The inappropriate choice of haemodialysis as a result of these misconceptions contributes to low peritoneal dialysis penetrance, increases transfer from peritoneal dialysis to haemodialysis, increases expenditure on haemodialysis and compromises quality of life for these patients. RECENT FINDINGS: Peritoneal dialysis is an excellent renal replacement modality that is simple, cost-effective and provides comparable clinical outcomes to conventional in-centre haemodialysis. Unfortunately, many patients are deemed unsuitable to start or continue peritoneal dialysis because of false or inaccurate beliefs about peritoneal dialysis. Here, we examine some of these 'myths' and critically review the evidence for and against each of them. We review the feasibility and risk of peritoneal dialysis in patients with prior surgery, ostomies, obesity and mesh hernia repairs. We examine the fear of mediastinitis with peritoneal dialysis after coronary artery bypass graft surgery and the belief that the use of hypertonic glucose dialysate causes peritoneal membrane failure. SUMMARY: By clarifying common myths about peritoneal dialysis, we hope to reduce overly cautious practices surrounding this therapy.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".