Prevalence of early childhood caries among 5‐year‐old children: A systematic review
Bibliographic record
Abstract
The aim of the present review was to describe the updated prevalence of early childhood caries (ECC) among 5-year-old children globally. Two independent reviewers performed a systematic literature search to identify English publications from January 2013 to December 2017 using MEDLINE, ISI Web of Science, and Scopus. Search MeSH key words were "dental caries" and "child, preschool". The inclusion criteria were epidemiological surveys reporting the caries status of 5-year-old children with the decayed, missing, and filled primary teeth (dmft) index. The quality of the publications was evaluated with the modified Newcastle-Ottawa Scale. Among the 2410 identified publications, 37 articles of moderate or good quality were included. Twenty of the included studies were conducted in Asia (China, India, Indonesia, Korea, Nepal, and Thailand), seven in Europe (Greece, Germany, Great Britain, and Italy), six in South America (Brazil), two in the Middle East (Saudi Arabia and Turkey), one in Oceania (Australia), and one in Africa (Sudan). The prevalence of ECC ranged from 23% to 90%, and most of them (26/37) were higher than 50%. The mean dmft score varied from 0.9 to 7.5. Based on the included studies published in the recent 5 years, there is a wide variation of ECC prevalence across countries, and ECC remains prevalent in most countries worldwide.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| 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".