Nutritional studies in patients with β-thalassemia major: A short review.
Bibliographic record
Abstract
BACKGROUND: Patients with β-thalassemia major (BTM) had variable prevalence of undernutrition and abnormal body composition. Methods: We performed an electronic search in PubMed, Scopus, Research gate, and Web of Sciences to evaluate the prevalence of nutritional disorders in patients with BTM worldwide in relation to their body composition and possible etiological factors. In addition, we reviewed the published nutritional intervention studies. Results: 22 studies on the prevalence of undernutrition (12 countries) and 23 nutritional intervention studies were analyzed. Undernutrition occurred in a considerable number of patients but varied greatly among different countries (from 5.2% to 70%). The lower middle income (LMI) countries (India, Pakistan, Iran, Egypt) had higher prevalence, while (high -middle and high income (Turkey, Greece, North America, USA, Canada) had lower prevalence. Even in patients with normal BMI, abnormalities of body composition are common with decreased muscle mass, lean-body mass, and bone mineral density. 65% to 75% of them had lower energy intake with low levels of circulating nutrients, minerals (zinc, selenium, and copper), and vitamins (D, E) versus controls. Increased macro and micronutrient requirements decreased absorption and /or increased loss or excretion are etiologic factors. Undernutrition was associated with short stature and lower quality of life (QOL). High prevalence of endocrinopathies, poor transfusion regimen (tissue hypoxia), improper chelation, and lack of maternal education, represented important risk factors in the production of poor growth in weight and stature. CONCLUSIONS: Timely detection of undernutrition in patients with BTM and proper nutritional intervention could prevent growth delay and comorbidities.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".