Prevalence of Anti-Erythropoietin Antibodies in Hemodialysis Patients without Clinical Signs of Pure Red Cell Aplasia
Bibliographic record
Abstract
BACKGROUND/AIMS: The prevalence of anti-erythropoietin antibodies in renal patients without clinical evidence of pure red cell aplasia (PRCA) who respond poorly to epoetin is unknown. This study tested for anti-erythropoietin antibodies in hemodialysis patients who were either hypo- or normoresponsive to epoetin treatment. METHODS: Epoetin hyporesponsiveness (hemoglobin < or =10.5 g/dl and epoetin > or =9,000 IU/week) and normoresponsiveness (hemoglobin >10.5 g/dl and epoetin <7,000 IU/week) were arbitrarily defined. Prevalence of anti-erythropoietin antibodies in hemodialysis patients without symptoms of PRCA was determined by screening sera of 536 patients from 35 German KfH dialysis units, using enzyme-linked immunosorbent assay (ELISA). Positive results were verified by radioimmunoprecipitation assay (RIP) and neutralizing activity was determined by bioassay. RESULTS: Anti-erythropoietin antibodies were detected in 3 hyporesponsive and 3 normoresponsive patients using ELISA. One patient per group was verified as borderline by RIP testing; the other 4 were negative. The bioassay was negative for 1 patient; the other died unrelated to PRCA before testing. Follow-up with RIP testing after 15 months under continuous epoetin treatment was negative (4 patients, 2 deceased). CONCLUSION: This survey did not identify anti-erythropoietin antibodies in hemodialysis patient's hyporesponsive to epoetin and does not support presumptive antibody screening as a routine work-up in these patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".