Effects of Erythropoietin Payment Policy on Cardiovascular Outcomes of Peritoneal Dialysis Patients: Observational Study
Bibliographic record
Abstract
BACKGROUND: The change in the reimbursement policy of erythropoietin administration to patients receiving peritoneal dialysis by the Taiwan National Health Insurance (NHI) system provided a natural experimental venue to examine whether cardiovascular risk differs when maintaining the hematocrit (Hct) level below or above 30%. OBJECTIVE: The aim of this study was to analyze the impact of loosening the erythropoietin payment criteria for peritoneal dialysis patients on their cardiovascular outcomes. METHODS: Two cohorts of incident peritoneal dialysis patients were identified according to the time before and after relaxation of the NHI's erythropoietin payment criteria, designated cohort 1 (n=1759) and cohort 2 (n=2981), respectively. The cohorts were matched according to propensity scores (1754 patients in each cohort) and then followed up for cardiovascular events, which were analyzed with Cox regressions. RESULTS: For the composite cardiovascular endpoint, patients in cohort 2 had a significantly lower risk than those in cohort 1. However, subgroup analysis showed that this risk reduction was observed only in patients with diabetes. CONCLUSIONS: After loosening erythropoietin payment criteria, reduced cardiovascular risks were observed, particularly for patients with diabetes. These results indicate that it is crucial to maintain an Hct level above 30% to reduce the cardiovascular risk in patients with diabetes undergoing peritoneal dialysis.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".