Associations of Prenatal Vitamin D status with Oral Health in Offspring: A Systematic Review.
Bibliographic record
Abstract
PURPOSE: The aim of this work is to evaluate the impact of prenatal vitamin D levels on oral health in offspring. MATERIALS AND METHODS: The search was carried out in three databases: MEDLINE (PubMed), ResearchGate and Wiley Online Library. The inclusion criteria were randomised controlled trials and cohort studies published between June 16, 2017 and June 16, 2022, laboratory assessment of prenatal vitamin D status and evaluation of primary or mixed dentition for observation of dental caries and developmental defects of enamel. The risk of bias for randomised controlled trials was analysed according to the Cochrane risk-of-bias tool and Newcastle-Ottawa scale was used to assess risk of bias for cohort studies. RESULTS: A total of 177 studies were identified, 11 were included in the data synthesis. Eight out of 11 studies were considered as high quality and the other 3 studies had moderate risk of bias. The synthesis of data revealed that the impact of prenatal vitamin D status on oral health in children is quite controversial and subsequent studies are necessary to examine whether vitamin D levels affect the risk of developing dental caries and enamel defects. CONCLUSION: The effect of prenatal vitamin D on oral health in offspring is not entirely clear. Since disturbances in dental hard tissues have a polyetiological origin, health specialists need to notify mothers about other possible risk factors and emphasise the importance of eating habits and individual oral hygiene in early childhood.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".