Micronutrient Deficiencies and Anemia in Children with Inflammatory Bowel Disease
Bibliographic record
Abstract
Children with inflammatory bowel disease (IBD) are at risk of developing nutrition deficiencies, particularly because of reduced intake, restrictive diets, malabsorption, and excessive nutrient loss. The aim of this study was to determine the prevalence and predictors of anemia and micronutrient deficiencies at diagnosis and one year follow up in children and adolescents with inflammatory bowel disease (IBD). Children and young adults diagnosed with IBD before the age of 17 years between 2012 and 2018 were included. Laboratory measurements including serum levels of iron, ferritin, zinc, vitamin D, vitamin A, vitamin E, selenium, copper, vitamin B12, and red blood cell (RBC) folate at diagnosis and one-year follow-up were documented as part of the Manitoba Longitudinal Pediatric Inflammatory Bowel Disease (MALPID) Cohort. A total of 165 patients with IBD were included, 87 (53%) with Crohn's disease (CD) and 78 (47%) with ulcerative colitis (UC). The prevalence of deficiencies in our cohort at diagnosis and one year follow-up, respectively, were iron (56% and 27%), ferritin (39% and 27%), zinc (10% and 6%), vitamin D (22% and 13%), vitamin A (25% and 25%), vitamin E (5% and 4%), selenium (10 and 7%), copper (17% and 27%), vitamin B12 (2% and 5%), and Red blood cell (RBC) folate (1% and 17%). Anemia was present in 57% and 25% at diagnosis and follow up respectively. In CD patients, age of diagnosis (15y-younger than 18y) was a predictor of moderate to severe anemia and albumin levels (<33 g/L) were protective against anemia. Many children with IBD suffer from anemia and micronutrient deficiencies at diagnosis and some fail to recover after one year despite being in clinical remission.
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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 | 0.000 | 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) | 0.001 | 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".