Chelation use and iron burden in North American and British thalassemia patients: a report from the Thalassemia Longitudinal Cohort
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it