Continuous ambulatory peritoneal dialysis—a guide to imaging appearances and complications
Bibliographic record
Abstract
OBJECTIVES: The aim of this article is to review and illustrate the typical imaging findings for a patient on continuous ambulatory peritoneal dialysis (CAPD) and its complications, examining the uses and limitations of multimodality imaging. BACKGROUND: CAPD is a commonly and increasingly used method of renal replacement therapy in end-stage renal failure (ESRF). From the set-up and insertion of the peritoneal catheter through to the actual treatment, there are pitfalls and complications that may adversely affect the patient and compromise the success of the dialysis. Complications can be either immediate or delayed, and can also be categorised into infectious and non-infectious aetiologies, including catheter failure, dialysate leaks, hernias and encapsulating sclerosing peritonitis. CONCLUSION: Early recognition of complications, both clinically and on the different imaging modalities, is essential in the management of CAPD in order to reduce treatment failure and limit patient morbidity and mortality. MAIN MESSAGES: Complications of peritoneal dialysis cause patient morbidity and treatment failure. Early recognition of complications from normal appearances is essential to limit dialysis failure. Multimodality imaging plays an important role in the diagnosis of these complications.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".