Effects of Vitamin D levels and supplementation on atopic dermatitis: A systematic review
Bibliographic record
Abstract
BACKGROUND: Atopic dermatitis (AD) is a chronic inflammatory skin condition affecting 5%-20% of children worldwide. Studies suggested both a correlation between serum vitamin D (VD) levels and AD severity and a therapeutic potential role for VD supplementation. OBJECTIVES: To determine whether serum VD levels correlate with AD severity and the effects of supplementation for disease improvement in children. DATA SOURCES: Ovid MEDLINE, EMBASE, and Cochrane Library databases were searched. STUDY SELECTION: Publications with children 0-18 years old with AD and data evaluating effects of VD levels or supplementation on AD severity were included. DATA EXTRACTION: Author, year, inclusion criteria, study design, location, age, VD levels, VD supplementation regimens, and baseline and final disease severities were extracted. RESULTS: Of the 21 included publications, 15, 5, and 1 evaluated VD level, VD supplementation, and both factors with disease severity, respectively. There were 4 randomized control trials (RCTs), 5 cohort, 6 case-control, and 6 cross-sectional studies. A significant inverse correlation between VD level and severity was described in 62.5% (10/16) of studies. There were 67% (4/6) that reported a significant improvement in AD severity with supplementation. LIMITATIONS: Studies meeting inclusion criteria were limited. Furthermore, papers were heterogeneous in terms of location, season, and VD supplementation regimen. Language and publication bias was another potential limitation. CONCLUSION: In children, the majority of existing literature confirmed a link between serum VD levels and AD severity. Weak evidence was found supporting improvement of AD with VD supplementation. Future large-scale studies are needed to support our findings.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".