Dental caries and vitamin D status in children in Asia
Bibliographic record
Abstract
Dental caries and vitamin D inadequacy are known to affect children worldwide. Vitamin D has a vital role in tooth formation. There is growing evidence linking suboptimal serum vitamin D level with dental caries in children. This paper reviews the literature on both the prevalence of dental caries and of vitamin D deficiency in children in four Asian regions, discusses their associated risk factors, and reviews the global evidence on the association between dental caries and vitamin D in children. Caries prevalence in children ranged from 40% to 97% in Eastern Asia, 38-73.7% in Southern Asia, and 26.5-74.7% in Western Asian countries. Moreover, a higher prevalence of vitamin D deficiency in Asian children was identified, even in countries in equatorial regions, ranging from 2.8% to 65.3% in Eastern Asia, 5-66.7% in Southern Asia, 4-45.5% in Western Asia and 38.1-78.7% in Central Asian countries. Obesity, age, female gender, higher latitude, season, darker skin pigmentation, sunlight protection behaviors, less sunlight exposure and low intake of food containing vitamin D were important factors associated with lower serum vitamin D in Asia. Suboptimal vitamin D level in children may be a significant risk factor for dental caries, and requires further research to ascertain such an association in children in Asia, as well as to understand its exact influence on caries risk and development.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.002 | 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".