Nutrient Insufficiencies/Deficiencies in Children With Sickle Cell Disease and Its Association With Increased Disease Severity
Bibliographic record
Abstract
BACKGROUND: Sickle cell disease (SCD) is characteristically described as a disease of hemolytic anemia and vaso-occlusive crises (VOCs). However, patients suffer from a multitude of other problems including impaired development, chronic pain, and increased susceptibility to infection. Nutritional deficiency has been implicated as a contributor to these issues. PROCEDURE: We reported the nutrition status with respect to vitamin D, zinc, B6, B12, folate, and homocysteine serum levels in Canadian children with SCD (n = 91). We also tested for associations between nutrients and markers of disease severity and growth. RESULTS: Almost half the sample (42%) had multiple nutrient insufficiencies/deficiencies, and a further 27% had a single insufficiency/deficiency. The most common insufficiency/deficiency was zinc in 57% followed by calcidiol (25 dihydroxyvitamin D (25(OH)D)) (52%). Sixteen percent of patients had low vitamin B6 levels, while folate, calcitriol (1,25(OH)D), and homocysteine levels were normal. Increased number of vitamin insufficiencies/deficiencies was associated with increasing disease severity (P = 0.018). Zinc insufficiency/deficiency was significantly associated with an increased number of home pain crises (P = 0.001) and an increased incidence of hospitalizations for VOCs (P = 0.01). CONCLUSIONS: Our findings show that patients with SCD commonly have multiple nutrient insufficiencies/deficiencies and support the growing evidence for the link between low zinc and increased VOC. It also indicates that increased nutrient insufficiencies/deficiencies are associated with increased disease severity in SCD. Prospective studies with larger samples are needed to further elucidate the relationship between nutrient deficiencies and SCD, and to determine whether nutrient supplementation can improve the disease course.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".