Ten things I wish I knew as a new peritoneal dialysis nurse
Bibliographic record
Abstract
A nurse new to home peritoneal dialysis (PD) undoubtedly has to learn all the steps for continuous ambulatory peritoneal dialysis (CAPD) and automated peritoneal dialysis (APD) procedures, along with basics such as hand hygiene, ordering supplies, disposing of supplies, recognizing signs and symptoms of peritonitis. However, it is not always clear what else the new PD nurse needs to know in order to successfully teach a patient all that a patient (and care partner) starting home PD training need to know, as well as to support that patient overtime once the patient is performing PD at home. To answer this question, using a modified Delphi technique, members of the International Society for Peritoneal Dialysis (ISPD) Nursing and Allied Health Professional Committee identified the top 10 practice advice (tips) these nurse members thought all new home PD nurses should know and be aware of. For each tip, we justified the importance of the tip and how it could be implemented. The 10 tips were quite varied and highlighted both the breadth and the depth of knowledge a new PD nurse needs to acquire over and above basic knowledge and skills such as performing CAPD and APD and recognizing signs and symptoms of peritonitis. The members of the ISPD Nursing and Allied Health Professional Committee who compiled this list of the top 10 tips, believe that through understanding the importance, justification, and implementation of each of these tips, the nurse new to a home PD program can, in turn, appreciate more how to individualize home PD training sessions, improve the quality of life for patients on PD, as well as extend the patients' time on PD.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".