Nursing management of catheter-related non-infectious complications of PD: Your questions answered
Bibliographic record
Abstract
A review from the last seven years (August 2016-July 2023) of questions posted to the International Society for Peritoneal Dialysis (ISPD) website "Questions about PD" by nurses and physicians from around the world revealed that 19 of the questions were associated with optimal approaches for preventing, assessing, and managing issues related to PD catheter non-infectious complications. Our review focused on responses to these questions whereby existing best practice recommendations were considered, if available, relevant literature was cited and differences in international practices discussed. We combined similar questions, revised both the original questions and responses for clarity, as well as updated the references to these questions. PD catheter non-infectious complications can often be prevented or, with early detection, the potential severity of the complication can be minimized. We suggest that the PD nurse is key to educating the patient on PD about PD catheter non-infectious complications, promptly recognize a specific complication and bring that complication to the attention of the Home Dialysis Team. The questions posted to the ISPD website highlight the need for more education and resources for PD nurses worldwide on the important topic of non-infectious complications related to PD catheters, thereby enabling us to prevent such complications as PD catheter malfunction, peri-catheter leakage and infusion or drain pain, as well as recognize and resolve these issues promptly when they do arise, thus allowing patients to extend their time on PD therapy and enhance their quality of life whilst 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 | 0.009 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".