Long-Term Follow-Up of 98 Cases of Recombinant Human Erythropoietin (epoetin) Induced Pure Red Cell Aplasia (PRCA): Outcomes and Treatment Data from the Research on Adverse Drug Reactions and Reports (RADAR) Program.
Bibliographic record
Abstract
Abstract Background: With the recent reporting of 191 cases of epoetin induced PRCA (Bennett CL, NEJM 2004), information is needed about the long-term follow-up of individuals with this diagnosis. While 47 cases from France, Germany, and England have been reported recently (Verhelst D, Lancet 2004), these data are limited by short follow-up and absence of information from other regions. Methods: RADAR investigators identified cases of epoetin-associated PRCA from the Food and Drug Administration MEDWatch database and proprietary reports from both the manufacturers epoetin alpha (Epogen from AMGEN, and Eprex from Johnson and Johnson) and epoetin beta (Neorecormon from Roche). Patients exposed to more than one epoetin form were excluded. PRCA recovery was operationally defined as transfusion independence, clearance of anti-erythropoietin antibody, and/or normalization of reticulocyte count. Results: See table. Conclusions: The overall recovery rate of 82% following transplantation versus 40% following immunosuppression suggests that transplantation might be the optimal therapy for epoetin-associated PRCA. Almost half of the patients rechallenged with epoetin developed recurrent PRCA, raising concern over the benefits of rechallenge. Eprex (N=82) Neorecormon (N=11) Epogen (N=5) Origin: US 60% 64% 100% Europe 60% 64% – Pacific Rim/Asia 21% 36% – Canada 19% – – Subcutaneous delivery 99% 100% 60% Recovery with Immunosuppression 37% 73% 0% Recovery with renal Transplant 11% – – No recovery with renal transplant 1% – 20% No Recovery 41% 18% 40% Unknown Recovery 10% 9% 40% Male gender 68% 73% 40% Rechallenged with epoetin N=12 – – Response rate 50% Median age 68 years 47 years 51 years
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".