Anemia management in chronic kidney disease and dialysis
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review describes the current state of anemia management with erythropoietin (EPO)-stimulating agents and iron supplementation in both chronic kidney disease and dialysis patients, with a focus on novel therapies. RECENT FINDINGS: We review the benefits and risks of EPO-stimulating agents, focusing on health-related quality of life and the uncertainties regarding optimal iron utilization in patients with kidney disease. We discuss novel therapies for iron supplementation including iron-based phosphate binders and dialysate iron delivery as well as alternatives to EPO-stimulating agents including hypoxia-inducible factor prolyl hydroxylase inhibitors. SUMMARY: Individualization of hemoglobin targets using EPO-stimulating agents and iron supplementation may be considered in younger, healthier patients with kidney disease to improve health-related quality of life. Optimal iron utilization in kidney disease patients is unclear, but novel iron base phosphate binders and dialysate iron delivery may play a role in intravenous iron avoidance and its potential complications. Phase 3 randomized controlled trials of hypoxia-inducible factor prolyl hydroxylase inhibitors are ongoing and are promising new alternatives to EPO-stimulating agents and their known adverse effects.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".