Calcium Intake in Children with Eczema and/or Food Allergy: A Prospective Cohort Study
Bibliographic record
Abstract
Eczema and food allergy may impact diet. Using data from a cohort of Manitoba children born in 1995, we examined calcium intake, defined as the frequency and quality of calcium products consumed (with the exception of cheese), amongst Manitoba adolescents (12-14 years) with eczema or food allergy in childhood (7-8 years) or adolescence. At both ages, children were assessed by a physician for eczema and food allergy. Adolescents completed food frequency questionnaires. Calcium intake was defined as 1+ vs. <1 weekly. Linear and logistic regression was used as appropriate, with adjustments for confounders. Overall, 468 adolescents were included, of whom 62 (13.3%) had eczema only in childhood, 25 (5.3%) had food allergy only, and 26 (5.6%) had eczema and food allergy. Compared to children without eczema, those with eczema only had poorer calcium intake in adolescence (β -0.44; 95%CI -0.96; 0.00). Girls, but not boys, with eczema in childhood had poorer calcium intake in adolescence than girls without eczema (β -0.84; 95%CI -1.60; -0.08). These patterns persisted even if children experienced transient vs. persistent eczema to adolescence. Similar but non-significant trends were found for food allergy. Childhood eczema is associated with significantly lower calcium intake and consumption in adolescence. These differences persist to adolescence, even if a child "outgrows" their allergic condition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".