Association Between the Level of Vitamin D and COVID-19 Infection in Children and Adolescents: A Systematic Review
Bibliographic record
Abstract
The COVID-19 pandemic has exerted a notable impact on worldwide health across diverse age groups. Although children and adolescents were initially considered less vulnerable, they have also shown susceptibility to the virus, emphasizing the importance of understanding associated risk factors. Epidemiological data reveal an increasing number of COVID-19 cases in this age group. The aim is to conduct a systematic assessment of the association between the level of vitamin D and COVID-19 infection in children and adolescents following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive literature search was performed across various databases up to October 7, 2023. Studies assessing laboratory-confirmed COVID-19 patients, the level of 25-hydroxyvitamin D in serum, and clinical outcomes were encompassed. Quality assessment was performed using the Newcastle-Ottawa Scale. Thirteen studies, conducted across six countries and involving 1,071 pediatric patients, were included. Vitamin D deficiency was prevalent among children and adolescents with COVID-19. Some studies suggested that vitamin D deficiency significantly increased the risk of COVID-19 infection and was linked to disease progression. Furthermore, deficiency in vitamin D demonstrated an association with increased levels of inflammatory markers, reduced lymphocyte counts, and heightened clinical symptoms, including fever and cough. Maintaining adequate vitamin D levels may be a crucial strategy for reducing COVID-19 severity and associated complications in children and adolescents. Nevertheless, there is a requirement for additional high-quality research to establish specific guidelines regarding vitamin D supplementation in this population amid the ongoing COVID-19 pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".