Leg Ulcers: A Report in Patients with Hemoglobin E Beta Thalassemia and Review of the Literature in Severe Beta Thalassemia
Bibliographic record
Abstract
BACKGROUND: Leg ulcers are a frequent complication in patients with the inherited hemoglobin disorders. In thalassemia, the literature is limited, and factors associated with the development of leg ulcers in hemoglobin E (HbE) beta thalassemia, the most common form of severe beta-thalassemia worldwide, have not previously been reported. METHODS: We reviewed all available medical records of patients with HbE beta thalassemia to document the onset of leg ulcers at the 2 largest treatment centers in Sri Lanka. We reviewed the literature to identify studies reporting outcomes of interventions for ulcers in severe thalassemia. RESULTS: Of a total of 255 actively registered patients with HbE thalassemia in the 2 centers, 196 patient charts were evaluable. A leg ulcer with a documented date of onset was recorded in 45 (22%) of 196 evaluable patients, aged (mean ± SEM) 22.2 ± 1.4 years. Most had been irregularly transfused; steady-state hemoglobin was 6.4 ± 0.2 g/dL. Treatment achieving healing in 17 patients included transfusions, antibiotics, oral zinc, wound toileting, and skin grafting. CONCLUSION: Leg ulcers may be more common in HbE beta thalassemia than in other forms of thalassemia. A systematic approach to treatment will be needed to document the prevalence and factors placing such patients at risk for leg ulcers. Controlled trials to evaluate the optimal treatment of this common complication are indicated.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".