The case for increased peritoneal dialysis utilization in low‐ and <scp>lower‐middle‐income</scp> countries
Bibliographic record
Abstract
Peritoneal dialysis (PD) has several advantages compared to haemodialysis (HD), but there is evidence showing underutilization globally, especially in low-income and lower-middle-income countries (LLMICs) where kidney replacement therapies (KRT) are often unavailable, inaccessible, and unaffordable. Only 11% of all dialysis patients worldwide use PD, more than 50% of whom live in China, the United States of America, Mexico, or Thailand. Various barriers to increased PD utilization have been reported worldwide including patient preference, low levels of education, and lower provider reimbursement. However, unique but surmountable barriers are applicable to LLMICs including the excessively high cost of providing PD (related to PD fluids in particular), excessive cost of treatment borne by patients (relative to HD), lack of adequate PD training opportunities for doctors and nurses, low workforce availability for kidney care, and challenges related to some PD outcomes (catheter-related infections, hospitalizations, mortality, etc.). This review discusses some known barriers to PD use in LLMICs and leverages data that show a global trend in reducing rates of PD-related infections, reducing rates of modality switches from HD, and improving patient survival in PD to discuss how PD use can be increased in LLMICs. We therefore, challenge the idea that low PD use in LLMICs is unavoidable due to these barriers and instead present opportunities to improve PD utilization in LLMICs.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".