Morbidity and mortality in chronically transfused subjects with thalassemia and sickle cell disease: A report from the multi‐center study of iron overload
Bibliographic record
Abstract
A natural history study was conducted in 142 Thalassemic (Thal), 199 transfused Sickle Cell Disease (Tx-SCD, n = 199), and 64 non-Tx-SCD subjects to describe the frequency of iron-related morbidity and mortality. Subjects recruited from 31 centers in the US, Canada or the UK were similar with respect to age (overall: 25 +/- 11 years, mean +/- SD) and gender (52% female). We found that Tx-SCD subjects were hospitalized more frequently compared with Thal or non-Tx-SCD (P < 0.001). Among those hospitalized, Tx-SCD adult subjects were more likely to be unemployed compared with Thal (RR = 1.6, 95% CI 1.0-2.5) or non-Tx-SCD (RR = 3.1, 95% CI 1.3-7.3). There was a positive relationship between the severity of iron overload, assessed by serum ferritin, and the frequency of hospitalizations (r= 0.20; P = 0.009). Twenty-three deaths were reported (6 Thal, 17 Tx-SCD) in 23.5 +/- 10 months of follow-up. Within the Tx-SCD group, those who died began transfusion (25.3 vs. 12.4 years, P < 0.001) and chelation therapy later (26.8 vs. 14.2 years, P = 0.01) compared with those who survived. The unadjusted death rate in Thal was lower (2.2/100 person years) compared with that in Tx-SCD (7.0/100 person years; RR = 0.38: 95% CI 0.12-0.99). However, no difference was observed when age at death was considered. Despite improvements in therapy, death rate in this contemporary sample of transfused adult subjects with Thal or SCD is 3 times greater than the general US population. Long term follow-up of this unique cohort of subjects will be helpful in further defining the relationship of chronic, heavy iron overload to morbidity and mortality.
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 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".