Demographics, clinical characteristics, and real-world treatment patterns among patients with beta-thalassemia: a retrospective medical record abstraction study
Bibliographic record
Abstract
Background: Beta-thalassemias (BTs) are characterized by deficient or absent synthesis of the beta-globin subunit, leading to anemia. Patient characteristics and treatment patterns in these patients may vary. Objective: This retrospective study evaluated demographics, clinical characteristics, and treatment patterns in patients with transfusion-dependent BT (TDT) and non-transfusion-dependent BT (NTDT). Methods: Medical records of adults with TDT or NTDT in the United Kingdom, France, Germany, Spain, and Canada with ⩾5 years of history within the practice were evaluated. Results: = 96), mean (SD) age was 36.6 (9.8) years, and 38.5% were female. Among patients with TDT, 21.2% received transfusions every 2 weeks or more frequently, 28.8% every 3 weeks, 26.3% every 4 weeks, and 21.2% less frequently than 4 weeks. Patients with TDT had a mean (SD) of 2.4 (0.6) units of blood transfused per transfusion, with a pretransfusion hemoglobin (Hb) level of 6.9 (1.3). In total, 84.4% of patients with NTDT had at least one transfusion, and the mean (SD) number of transfusions among patients with NTDT was 15.9 (15.9). Among patients with NTDT, the mean (SD) units of blood per transfusion were 2.2 (0.6) units, and the mean (SD) Hb level prior to transfusion was 7.4 (1.2) g/dL. Iron chelation therapy was received by 70.3% of TDT patients and 45.8% of NTDT patients. Conclusion: This study found that both patients with NTDT and TDT have low pretransfusion Hb levels. A high number of patients, especially patients with TDT, were not treated according to the current recommendations on target hemoglobin level, thereby highlighting the importance of national reference centers for improving long-term outcomes and quality of life in these patients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".