Thalassaemia in children: from quality of care to quality of life
Bibliographic record
Abstract
Over the past few decades, there has been a remarkable improvement in the survival of patients with thalassaemia in developed countries. Availability of safe blood transfusions, effective and accessible iron chelating medications, the introduction of new and non-invasive methods of tissue iron assessment and other advances in multidisciplinary care of thalassaemia patients have all contributed to better outcomes. This, however, may not be true for patients who are born in countries where the resources are limited. Unfortunately, transfusion-transmitted infections are still major concerns in these countries where paradoxically thalassaemia is most common. Moreover, oral iron chelators and MRI for monitoring of iron status may not be widely accessible or affordable, which may result in poor compliance and suboptimal iron chelation. All of these limitations will lead to reduced survival and increased thalassaemia-related complications and subsequently will affect the patient's quality of life. In countries with limited resources, together with improvement of clinical care, strategies to control the disease burden, such as public education, screening programmes and appropriate counselling, should be put in place. Much can be done to improve the situation by developing partnerships between developed countries and those with limited resources. Future research should also particularly focus on patient's quality of life as an important outcome of care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".