Peritoneal Dialysis: Better than, Equal to, or Worse than Hemodialysis? Data Worth Knowing before Choosing a Dialysis Modality
Bibliographic record
Abstract
Technological advances such as those that allow the delivery of an adequate dialysis dose to a larger percentage of patients, minimization of peritoneal membrane damage with more biocompatible solutions, and lower peritonitis rates will undoubtedly improve retention of patients on peritoneal dialysis (PD) for longer periods. Currently, only 15% of the world dialysis population is managed by PD. Peritoneal dialysis has many advantages over hemodialysis, and if end-stage renal disease (ESRD) patients are fully informed about them, the proportion of patients who would prefer this treatment would rise to 25%-30%. An integrated approach to the treatment of ESRD could start with PD in a large percentage of patients, especially those who will receive a kidney transplant within 2 - 3 years. With the present epidemic of ESRD, this approach could lead to a significant saving, relieve the pressure on dialysis units, and allow a larger number of ESRD patients to be treated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.017 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".