Disparity in the management of iron overload between patients with sickle cell disease and thalassemia who received transfusions
Bibliographic record
Abstract
BACKGROUND: Transfusion therapy is frequently used to prevent morbidity in sickle cell disease (SCD), and subsequent iron overload is common. The objective of this study was to evaluate the current standard of care in monitoring iron overload and related complications in patients with SCD compared to thalassemia (Thal). STUDY DESIGN AND METHODS: A cross-sectional study was conducted at 31 hematology clinics in the United States, Canada, or the United Kingdom. Patients who received transfusions with a mean serum ferritin level of least 2000 ng per mL were eligible. A total of 199 patients with SCD (113 female; 24.9 +/- 13.2 years) and 142 with Thal (66 female; 25.8 +/- 8.1 years) were recruited, and data were collected between 2001 and 2003 by interview and medical record review. RESULTS: Although both groups were recruited on the basis of significant iron overload, the likelihood of performing a liver biopsy for routine iron monitoring was significantly higher (odds ratio [OR], 3.4; 95% confidence interval [CI], 2.2-5.3) in Thal than SCD. Thal patients were also more likely to be screened for iron-related organ injury including an echocardiograph for cardiomyopathy (OR, 2.6; p < 0.001; 95% CI, 1.6-4.2), alanine aminotransferase for liver function (OR, 8.3; CI, 1.05-64.4), and thyroid-stimulating hormone for hypothyroidism (OR, 12.3; CI, 7.0-21.5). For adult SCD patients, those maintained on simple transfusion with a serum ferritin level of greater than 2500 ng per mL were the least likely to have a liver biopsy (p < 0.03). CONCLUSIONS: These data highlight the unsystematic monitoring of iron and related organ injury in SCD. Until the relationship between iron and related comorbidities is better understood, routine monitoring of iron overload in SCD patients who receive transfusions should be considered a standard part of clinical care.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".