Bibliographic record
Abstract
Over the past four decades, there have been dramatic improvements in survival for patients with thalassemia major due in large measure to improved iron chelators. Two chelators are approved for use in the United States and Canada, parenteral deferoxamine and oral deferasirox. Three are available in much of the rest of the world, where oral deferiprone is also approved (in the United States, deferiprone is only available in studies, for emergency use, or on a "compassionate-use" basis). Many trials and worldwide clinical experience demonstrate that each of the three drugs can chelate and remove iron, and thereby prevent or improve transfusional hemosiderosis in thalassemia patients. However, the chelators differ strikingly in side-effect profile, cost, tolerability and ease of adherence, and (to some degree) efficacy for any specific patient. The entire field of chelator clinical trials suffers from the fact that each drug (as monotherapy or in combination) has not been tested directly against all of the other possibilities. Acknowledging the challenges of assessing chelators with diverse properties and imperfect comparative data, the purpose of this review is to summarize the last 4 years of studies that have improved our understanding of the applications and limitations of iron chelators in various settings for thalassemia patients, and to point out areas for much-needed future research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".