Serum Vitamins and Minerals at Diagnosis and Follow‐up in Children With Celiac Disease
Bibliographic record
Abstract
OBJECTIVES: Children with celiac disease (CD) may experience deficiencies of several micronutrients. The objectives of the present study were to determine the prevalence of micronutrient deficiencies in children with CD at diagnosis, 6 months, and 18 months after the start of a gluten-free diet (GFD), and examine any correlation between micronutrient deficiencies, serum tissue transglutaminase (TtG) immunoglobulin A (IgA) antibody titers, and the degree of mucosal damage at diagnosis. METHODS: Children (<17 years) with CD had their serum vitamins, minerals, and anti-TtG IgA antibodies measured at diagnosis, 6 and 18 months after starting a GFD. Histopathological changes of duodenal biopsies at diagnosis were documented using modified MARSH classification. RESULTS: The medical records of 140 children (mean age at diagnosis 7.8 ± 4.01 years, 87 girls [621%]) with CD were examined. At diagnosis, serum vitamin D was the most commonly deficient vitamin in 70% of children. Serum ferritin was subnormal in 34.5% with zinc in 18.6% children but only 12 (10.9%) children had iron deficiency anemia. There was no correlation between micronutrient deficiencies at diagnosis and serum TtG IgA antibody titers or the degree of villous atrophy. The majority of serum levels of measured micronutrients had normalized after 6 months of starting GFD except for vitamin D, which improved but remained subnormal. CONCLUSIONS: At diagnosis, most children with CD have vitamin D deficiency. The degree of micronutrient deficiencies does not correlate with the degree of villous atrophy or serum titers of anti-TtG IgA antibodies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".