Restrictive lung disease in β-thalassemia major is associated with myocardial iron overload
Bibliographic record
Abstract
Background: Pulmonary dysfunction has been reported in patients with β-thalassaemia major but data are conflicting and the association with iron overload remains unclear. Objectives: To determine the pattern of pulmonary dysfunction in patients with β-thalassaemia major and their associations with iron overload. Methods: Subjects with β-thalassaemia major were recruited for lung function assessment. Serum ferritin and magnetic resonance imaging (MRI) measurements of iron status of the myocardium and the liver were used as surrogate indexes of body iron content. A subgroup of this cohort provided data on the longitudinal progress of their lung function. Results: One hundred and one patients were recruited with a mean age of 25.1 years (SD 7.9 years). Thirty-eight (38%) and five (5%) had restrictive and obstructive lung function deficits, respectively. There was a significant correlation between MRI myocardial T2* relaxation time and forced vital capacity (r=0.291, p=0.048). Higher MRI cardiac T2* relaxation time was associated with lower risk of having restrictive lung function deficit (Odds ratio (OR): 0.94; 95% CI: 0.89-0.99; p=0.023) after adjusting for age, gender and BMI. Twenty-three subjects underwent lung function reassessment with a mean follow-up duration of 13 years. Overall, they did not demonstrate significant changes in pulmonary function over time, 3 patients who had normal lung function at baseline developed restrictive abnormality at follow-up. Conclusions: Restrictive lung disease is prevalent in patients with β-thalassaemia major, and the severity correlates with myocardial iron overload. Monitoring of lung function in this group of patients is important, particularly for those with iron overload.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".