Pharmacologic adjuvants to epoetin in the treatment of anemia in patients on hemodialysis
Bibliographic record
Abstract
Anemia is a common complication of chronic kidney disease, particularly in patients who are on dialysis. The use of recombinant human erythropoietin has led to the eradication of severe anemia in the dialysis population. Correction of anemia in these patients has been associated with better quality of life and clinical outcomes. Some hemodialysis patients have anemia that either is relatively refractory to epoetin therapy or requires very high doses of epoetin (i.e., hyporesponsiveness), despite having adequate iron stores, and are thus unable to achieve or maintain target hemoglobin levels. Several pharmacologic agents have been studied for effects on improving response to epoetin, either to counter hyporesponsiveness or simply to reduce epoetin use for purely economic reasons. This review examines the available literature regarding the efficacy of these potential pharmacologic adjuvants to epoetin in the treatment of anemia in patients on maintenance hemodialysis, with special emphasis on androgens, vitamin C (ascorbic acid), and L-carnitine. A review of published guidelines and recommendations for use of these agents in hemodialysis patients is provided.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".