Bibliographic record
Abstract
ABSTRACT: Although clinical benefits of iron chelation therapy (ICT) in red blood cell (RBC) transfusion-dependent (TD) hereditary anemias such as α-thalassemia major are incontrovertible, the evidence supporting a similar benefit in patients with TD myelodysplastic neoplasms (MDS) and iron overload (IOL) is sometimes debated. MDS presents later in life, has a limited repertoire of life-extending therapies, and patients may have comorbidities acting as competing causes of death. However, refined prognostication identifies patients with MDS with a reasonable life expectancy, and because 50% of patients will ultimately become RBC TD and develop transfusional IOL, ICT should be considered in some. Using illustrative cases, we summarize mechanisms of iron toxicity, strategies for the identification of IOL, and propose definitions of IOL severity. We provide rationale for, and recommend which patients may benefit from, ICT. We discuss currently available chelators, their administration, monitoring, side effects, and their management. Given challenges with the use of iron chelators, we suggest the nuances to be considered when planning chelation initiation to include the rate of iron accumulation, the presence of organ iron and/or dysfunction, and detectable indicators of oxidative stress. Areas for future investigation are identified.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".