Vitamin D supplementation in primary allergy prevention: Systematic review of randomized and non‐randomized studies
Bibliographic record
Abstract
BACKGROUND: To date, a systematic review of the evidence regarding the association between vitamin D and allergic diseases development has not yet been undertaken. OBJECTIVE: To review the efficacy and safety of vitamin D supplementation when compared to no supplementation in pregnant women, breastfeeding women, infants, and children for the prevention of allergies. METHODS: Three databases were searched through January 30, 2016, including randomized (RCT) and nonrandomized studies (NRS). Two reviewers independently extracted data and assessed the certainty in the body of evidence using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. RESULTS: Among the 1932 articles identified, one RCT and four NRS were eligible. Very low certainty in the body of evidence across examined studies suggests that vitamin D supplementation for pregnant women, breastfeeding women, and infants may not decrease the risk of developing allergic diseases such as atopic dermatitis (in pregnant women), allergic rhinitis (in pregnant women and infants), asthma and/or wheezing (in pregnant women, breastfeeding women, and infants), or food allergies (in pregnant women). We found no studies of primary prevention of allergic diseases in children. CONCLUSION: Limited information is available addressing primary prevention of allergic diseases after vitamin D supplementation, and its potential impact remains uncertain.
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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 | 0.023 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".