Teaching peritoneal dialysis: A position paper for the International Society for Peritoneal Dialysis
Bibliographic record
Abstract
Given the central importance of the peritoneal dialysis (PD) nurse in successfully training and supporting a patient with PD at home, as well as preventing complications as a result of the therapy, the International Society for Peritoneal Dialysis (ISPD) has provided guidance on the principles for training in two previous publications. Despite the lack of high certainty evidence in teaching PD, this ISPD 2025 Position Paper builds upon the two prior works to provide contemporary approaches to training a patient/care partner for PD to be performed at home, based upon an evolution in healthcare practices broadly and the cumulative evidence to support recommendations to date. A number of practice points have been provided. Suggestions are discussed on key areas in teaching PD which include: (a) Education, knowledge, skills and attributes for the PD nurse trainer; (b) Preparation for the training; (c) Methods of training/educational interventions; (d) Post training; and (e) Measures of outcomes. Areas for future research are suggested and include: best practices on educational interventions; knowledge and skills necessary for PD nurses; and how to best capture and measure the patient experience related to PD training.
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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.017 | 0.020 |
| Insufficient payload (model declined to judge) | 0.014 | 0.012 |
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".