Chelation use and iron burden in North American and British thalassemia patients: a report from the Thalassemia Longitudinal Cohort
Bibliographic record
Abstract
Morbidity and mortality in thalassemia are associated with iron burden. Recent advances in organ-specific iron imaging and the availability of oral deferasirox are expected to improve clinical care, but the extent of use of these resources and current chelation practices have not been well described. In the present study, we studied chelation use and the change in iron measurements in 327 subjects with transfusion-dependent thalassemia (mean entry age, 22.1 ± 2.5 years) from 2002-2011, with a mean follow-up of 8.0 years (range, 4.4-9.0 years). The predominant chelator currently used is deferasirox, followed by deferoxamine and then combination therapies. The use of both hepatic and cardiac magnetic resonance imaging increased more than 5-fold (P < .001) during the study period, leading to an 80% increase in the number of subjects undergoing liver iron concentration (LIC) measurements. Overall, LIC significantly improved (median, 10.7 to 5.1 mg/g dry weight, P < .001) with a nonsignificant improvement in cardiac T2* (median, 23.55 to 34.50 ms, P = .23). The percentage of patients with markers of inadequate chelation (ferritin > 2500 ng/mL, LIC > 15 mg/g dry weight, and/or cardiac T2* < 10 ms) also declined from 33% to 26%. In summary, increasing use of magnetic resonance imaging and oral chelation in thalassemia management has likely contributed to improved iron burden.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".