Optimal tooth brushing initiation age and frequency for preventing early childhood caries: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Early childhood caries (ECC) is a prevalent global public health concern. Although daily tooth brushing is a key preventive measure, uncertainty remains regarding the ideal age to initiate tooth brushing and the optimal brushing frequency to minimise ECC risk in preschool children. A systematic review and meta-analysis were conducted to explore the association between tooth brushing practices (initiation age and frequency) and the early childhood caries among children under 6 years of age. METHODS: We searched three databases (Proquest, Web of Science, and Scopus) for observational studies (2000-2024). Quality was assessed with the Newcastle-Ottawa Scale, and random-effects meta-analyses (DerSimonian-Laird) were conducted. Heterogeneity (I2), publication bias (Egger's test), and sensitivity analyses (outlier exclusion) were performed. RESULTS: Thirty-two studies (27 cross-sectional, 5 longitudinal) out of 2833 initially identified records met inclusion criteria. 10 contributed to the brushing initiation meta-analysis and 8 to the frequency analysis. Including all 10 studies, late initiation showed a borderline nonsignificant pooled odds ratio (OR = 1.40, 95% CI: 0.91-2.16, p = 0.13) with I2 > 90%. Excluding one outlier yielded a significant OR = 1.75 (1.50-2.03, p < 0.0001). Brushing < 2 × /day was associated with 2.11-fold higher ECC odds (p = 0.0052), also with heterogeneity but no evidence of publication bias (Egger's p = 0.1161). CONCLUSIONS: Despite considerable between-study variability, the overall evidence suggests that commencing tooth brushing by 12 months of age and brushing at least twice daily maybe protective against ECC, highlighting the importance of integrating these practices into early childhood health programs.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.046 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".