Iron Overload and Iron-Chelating Therapy in Hemoglobin E-β Thalassemia
Bibliographic record
Abstract
Whereas hemoglobin (Hb) E-beta thalassemia is recognized as probably the most common serious hemoglobinopathy worldwide, its natural history remains poorly defined. The interaction of hemoglobin E and beta-thalassemia result in a wide spectrum of clinical disorders, some indistinguishable from thalassemia major and some milder and not transfusion-dependent. Partially as a result of this wide range of phenotypes, clear guidelines for approaches to transfusion and to iron-chelating therapy for patients with Hb E-beta thalassemia have not been developed. By contrast, data that have accumulated during the past 10 years in patients with beta-thalassemia permit a quantitative approach to the management of iron overload and provide guidelines for the control of body iron burden in individual patients treated with iron-chelating therapy. These guidelines may be applicable to patients with Hb E-beta thalassemia. Preliminary evidence from our studies of iron loading in affected patients with Hb E-beta thalassemia in Sri Lanka suggest that this disorder may be associated with variable, but accelerated, gastrointestinal iron absorption, and that the iron loading associated with chronic transfusions in patients with Hb E-beta thalassemia is similar to that observed in patients with beta-thalassemia. These data, in the only cohort of patients with Hb E-beta thalassemia to have undergone quantitative assessment of body iron burden, suggest that the principles that guide assessment of iron loading and initiation of chelating therapy in patients with beta-thalassemia may be generally applicable to those with Hb E-beta thalassemia. Further quantitative studies in both nontransfused and transfused patients will be necessary to permit firm conclusions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".