Deferiprone vs deferoxamine for transfusional iron overload in SCD and other anemias: a randomized, open-label noninferiority study
Bibliographic record
Abstract
Many people with sickle cell disease (SCD) or other anemias require chronic blood transfusions, which often causes iron overload that requires chelation therapy. The iron chelator deferiprone is frequently used in individuals with thalassemia syndromes, but data in patients with SCD are limited. This open-label study assessed the efficacy and safety of deferiprone in patients with SCD or other anemias receiving chronic transfusion therapy. A total of 228 patients (mean age: 16.9 [range, 3-59] years; 46.9% female) were randomized to receive either oral deferiprone (n = 152) or subcutaneous deferoxamine (n = 76). The primary endpoint was change from baseline at 12 months in liver iron concentration (LIC), assessed by R2* magnetic resonance imaging (MRI). The least squares mean (standard error) change in LIC was -4.04 (0.48) mg/g dry weight for deferiprone vs -4.45 (0.57) mg/g dry weight for deferoxamine, with noninferiority of deferiprone to deferoxamine demonstrated by analysis of covariance (least squares mean difference 0.40 [0.56]; 96.01% confidence interval, -0.76 to 1.57). Noninferiority of deferiprone was also shown for both cardiac T2* MRI and serum ferritin. Rates of overall adverse events (AEs), treatment-related AEs, serious AEs, and AEs leading to withdrawal did not differ significantly between the groups. AEs related to deferiprone treatment included abdominal pain (17.1% of patients), vomiting (14.5%), pyrexia (9.2%), increased alanine transferase (9.2%) and aspartate transferase levels (9.2%), neutropenia (2.6%), and agranulocytosis (0.7%). The efficacy and safety profiles of deferiprone were acceptable and consistent with those seen in patients with transfusion-dependent thalassemia. This trial study was registered at www://clinicaltrials.gov as #NCT02041299.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".