PDOPPS International Anemia Prevalence and Management in People Receiving Peritoneal Dialysis
Bibliographic record
Abstract
Background: Optimal anemia management in peritoneal dialysis (PD) remains uncertain, with substantial variation in clinical practice across countries. We aimed to describe the evolution over time of iron indices in people receiving PD, the determinants of this and the associations of these indices with mortality. Methods: We analyzed baseline data from adult PD patients enrolled in the Peritoneal Dialysis Outcomes and Practice Patterns Study across seven countries: Australia/New Zealand, Japan, USA, UK, Thailand, and Canada. Cross-sectional comparisons were made for hemoglobin, ferritin, transferrin saturation (TSAT), serum iron, total iron-binding capacity (TIBC), and use of erythropoiesis-stimulating agents and iron (oral or intravenous). Time-varying Cox models with restricted cubic splines were used to examine associations of iron, TSAT, TIBC and ferritin with all-cause, cardiovascular and infection-related mortalities. Results: The analysis included 7,930 patients. Time-varying Cox models demonstrated U-shaped associations between serum iron, TSAT and TIBC with all-cause, cardiovascular, and infection-related mortality. Ferritin showed a monotonic increase in hazard, particularly for all-cause and cardiovascular mortality, likely reflecting its dual role as a marker of iron stores and inflammation. These findings support a multi-marker approach to iron assessment, rather than reliance on ferritin alone. Conclusion: High and low levels of iron, TSAT, and TIBC were linked to increased mortality. In contrast, ferritin showed a steady increase in hazard, particularly for all-cause and cardiovascular mortality, likely due to its inflammatory nature. These findings suggest ferritin is a poor standalone marker of iron status in peritoneal dialysis patients.Association between iron biomarkers and mortality
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".