Peritoneal dialysis: an underutilized modality
Bibliographic record
Abstract
PURPOSE OF REVIEW: There have been differential changes in outcomes of patients treated with in-center hemodialysis and peritoneal dialysis. In light of these changes, providers and practices should reevaluate the utilization of peritoneal dialysis. RECENT FINDINGS: Accumulating evidence confirms that the present distribution of dialysis modality in the United States does not reflect patient choice. Furthermore, in most recent cohorts, the 5-year adjusted survival of patients treated with hemodialysis and peritoneal dialysis is remarkably similar (35 and 33% respectively). Similar results have been reported from Canada, Australia, and New Zealand. Moreover, health-related quality of life of peritoneal dialysis patients are no different from that reported by those treated with nocturnal hemodialysis. Finally, an expansion of use of peritoneal dialysis for the treatment of end-stage renal disease makes economic sense for the taxpayers - the payors for dialysis services. SUMMARY: The improvement in outcomes of peritoneal dialysis patients makes a compelling argument for the expansion of the use of the therapy for the treatment of end-stage renal disease in the United States. We think that 20-40% of patients can be treated with peritoneal dialysis. However, any expansion in use should be done gradually and should include training healthcare providers while continuously monitoring patient outcomes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".