Vascular Access Planning in Peritoneal Dialysis Patients
Bibliographic record
Abstract
Peritoneal dialysis (PD) is a well-established renal replacement therapy for end-stage renal disease patients. Nonetheless, on an annual basis, at least 10% of patients shift from PD to hemodialysis for a variety of reasons. Thus the issue of vascular access creation needs to be addressed for this small but significant group of patients. Despite the relatively consistent number of dropouts, the creation of an arteriovenous fistula prior to transfer remains suboptimal, and variable from center to center. Literature for this specific area is poor and dated. Guidelines seem to suggest vascular access creation in high-risk failure patients, but they have no detailed criteria to select patients that would likely fail PD and therefore take advantage of a backup access. There is a need to better understand and predict patients that require conversion to hemodialysis to develop a plan that focuses on wellness and maximum quality of life in the lifecycle of PD patients. This review addresses the issue of vascular access planning in adult PD patients, presents the available literature on the topic and the current guidelines and recommendations, and describes a research agenda to guide decision making in clinical practice.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".