A meta-analysis of the relative doses of erythropoiesis-stimulating agents in patients undergoing dialysis
Bibliographic record
Abstract
Background. Erythropoiesis-stimulating agents (ESAs) such as epoetin alfa and beta, and darbepoetin alfa have improved the management of anaemia secondary to chronic kidney disease. Numerous studies have reported a dose reduction when patients receiving dialysis were converted from epoetin to darbepoetin alfa using the starting dose conversion of 200:1 as indicated on the prescribing label by the European Medicines Agency. The objective of this meta-analysis was to summarize the existing body of scientific evidence to evaluate the potential dose savings when comparing epoetin alfa or beta to darbepoetin alfa. Method. Medline and EmBase were searched to identify all published trials investigating ESA treatment in anaemic patients receiving dialysis and converted from epoetins to darbepoetin alfa. We selected prospective randomized controlled, non-randomized and observational studies involving patients on dialysis that compared epoetin and darbepoetin alfa dosing. Results. Of 573 articles identified, 9 studies met the eligibility criteria and were included in our analysis. The overall percentage dose savings attained when dialysis patients were converted from epoetin to darbepoetin alfa was 30% (range: 4%-44%). Greater dose savings were noted with intravenous administration (33%) compared with subcutaneous (27%) and between switch-over studies (31%) and RCTs (27%). In all studies, target haemoglobin levels were maintained before and after conversion. Conclusion. This meta-analysis demonstrates that when using an initial 200:1 conversion ratio, as indicated on the European label, from epoetin to darbepoetin, a subsequent reduction in dose was observed and an average 30% dose savings could be achieved.
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.065 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".