Beyond Bones : A review of pre-natal vitamin D levels and allergy development in children
Bibliographic record
Abstract
Although the connection between vitamin D, the immune system, and allergies encompass a large area of research, a consensus about the specifics of the vitamin’s role in the developing immune system has yet to be reached. At the same time, prevalence of allergies and hypersensitization has seen a dramatic increase in the last 50 years, especially in developed nations, with very little understanding as to why. Some have connected the so-called allergy pandemic to vitamin D, placing the blame on either the similarly concerning vitamin D deficiency prevalence or the rise in supplementation of the vitamin (and thus, an argued excess). Through conducting a non-exhaustive review of the literature, the aim of this paper was to answer the question: Does prenatal vitamin D insufficiency or exposure result in increased risk of developing an atopic condition, including eczema, asthma, and food allergy sensitization? For food allergies and eczema, there is support for both increased and decreased exposure causing each condition, as well as support for vitamin D having no effect at all. The debate around asthma is more specific and focuses on whether elevated vitamin D has a detrimental effect or not. The current evidence is largely based in observational research and findings are still very inconsistent, but as new intervention studies are conducted, a more definitive answer should eventually emerge.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".