Predictive factors for the diagnosis of coeliac disease in children and young people in primary care: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Coeliac Disease (CD) often has its onset in childhood and affects 1% of the population. This review aimed to identify important predictive factors for coeliac disease in children and young people which could help GPs decide when to offer testing. METHODS: We searched MEDLINE, Embase and Cochrane Library to April 2024. Included studies were observational or randomized trials reporting the risk of CD when exposed to predictive factor(s) in people ≤25 years of age. Genetic factors were excluded. Risk of Bias was assessed using the Newcastle-Ottawa Scale. Random effects meta-analysis was performed for factors reported in ≥5 studies to calculate pooled odds ratios (OR) or standardized mean differences (SMD). RESULTS: Of 11,623 unique abstracts, 183 were included reporting on 140+ potentially associated factors. Meta-analyses of 28 factors found 14 significant associations with CD diagnosis: having type 1 diabetes (OR 8.70), having a first degree relative with coeliac disease (OR 5.19), being of white ethnicity (OR 2.56), having thyroid disease (OR 2.16), being female (OR 1.53), more frequent gastroenteritis in early childhood (OR 1.48), having frequent respiratory infections in early childhood (OR 1.47), more gluten ingestion in early life (OR 1.25), having more infections in early life (OR 1.22), antibiotic use in early childhood (OR 1.21), being born in the summer (OR 1.09), breastfeeding (OR 0.79) older age at diagnosis of type 1 diabetes (OR 0.64), and heavier weight (SMD -0.21). The final three were associated with lower risk of CD diagnosis. DISCUSSION: This is the first systematic review and meta-analysis of predictive factors for CD in children. Amongst the 14 factors we identified that were significant, three were potentially modifiable: breast feeding, antibiotic use and amount of gluten ingestion in early childhood. This work could inform the development of clinical support tools to facilitate the early diagnosis of CD.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.039 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".