Predictors of iron versus erythropoietin responsiveness in anemic hemodialysis patients
Bibliographic record
Abstract
Anemia protocols for hemodialysis patients usually titrate erythropoietin (ESA) according to hemoglobin and iron according to a threshold of ferritin, with variable response seen. A universally optimum threshold for ferritin may be incorrect, and another view is that ESA and iron are alternative anemia treatments, which should be selected based on the likely response to each. Hemodialysis patients developing moderate anemia were randomised to treatment with either an increase in ESA or a course of intravenous iron. Over 2423 patient-months in 197 patients, there were 133 anemia episodes with randomized treatment. Treatment failure was seen in 20/66 patients treated with ESA and 20/67 patients treated with iron (30.3 vs. 29.9%, p = 1.0). Successful ESA treatment was associated with lower C-reactive protein (13.5 vs. 28.6 mg/L, p = 0.038) and lower previous ESA dose (6621 vs. 9273 μg/week, p = 0.097). Successful iron treatment was associated with lower reticulocyte hemoglobin (33.8 vs. 35.5 pg, p = 0.047), lower hepcidin (91.4 vs. 131.0 μg/ml, p = 0.021), and higher C-reactive protein (29.5 vs. 12.6 mg/L, p = 0.085). A four-variable iron preference score was developed to indicate the more favorable treatment, which in a retrospective analysis reduced treatment failure to 17%. Increased ESA and iron are equally effective, though treatment failure occurs in almost 30%. Baseline variables including hepcidin can predict treatment response, and a four-variable score shows promise in allowing directed treatment with improved response rates.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".