Simplified caries prediction models (>=2 predictors; microbiome-primary studies excluded), but some pooled models include microbe predictors (Yaxuan 2023: S. mutans; Kim 2018: biofilm acidogenicity/saliva) vs No competing model: Model discrimination (area under the ROC curve, AUC)
Simplified childhood caries prediction models show moderate pooled discrimination (AUC 0.79, 95% CI 0.73-0.84) but with very high heterogeneity (I2=97.3%) and all models at high risk of bias (PROBAST); ungraded.
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Simplified caries prediction models (>=2 predictors; microbiome-primary studies excluded), but some pooled models include microbe predictors (Yaxuan 2023: S. mutans; Kim 2018: biofilm acidogenicity/saliva) vs None (no competing model) Model discrimination (area under the ROC curve, AUC) · Children and adolescents aged 1-18 years · Prediction of future/undiagnosed caries; cohort follow-up 3 months-10 years, Home/community preventive oral healthcare; detection of undiagnosed caries in asymptomatic children, Overall (6 validated models pooled) Simplified childhood caries prediction models show moderate pooled discrimination (AUC 0.79, 95% CI 0.73-0.84) but with very high heterogeneity (I2=97.3%) and all models at high risk of bias (PROBAST); ungraded. Risk prediction models for dental caries in children and adolescents: a systematic review and meta-analysis. (2025) All outcomes | AUC 0.79 [0.73, 0.84] | 6 studies | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Risk prediction models for dental caries in children and adolescents: a systematic review and meta-analysis. 2025 All outcomes | 18 January 2024 | 16 | Not applicable |
Studies behind the findings
The primary studies the full-text reviews tie to this outcome. Reviews with headline outcomes only do not list them here.
- Prediction model for early childhood caries risk based on behavioral determinants using a machine learning algorithm · 2022 · included by 1 of these review
- Identifying high-risk children for dental caries in school settings: A simple predictive model · 2022 · included by 1 of these review
- Prediction model for early childhood caries risk based on behavioral determinants using a machine learning algorithm. · 2022 · included by 1 of these review
- A predictive nomogram: a cross-sectional study on a simple-to-use model for screening 12-year-old children for severe caries in middle… · 2021 · included by 1 of these review
- Nomogram prediction of caries risk among schoolchildren age 7 years based on a cohort study in Shanghai. · 2021 · included by 1 of these review
- Simplified Prediction Model for Accurate Assessment of Dental Caries Risk among Participants Aged 10-18 Years. · 2018 · included by 1 of these review
- 25-Hydroxyvitamin D3 metabolism in a human leukemia cell line. · 1986 · included by 1 of these review
- Kim 2018 · included by 1 of these review
How reviews are matched to this question
Reviews word the same question differently. Four decision models (TypeSafe's Jev, Liquid AI's d1-ultra, SubQ Snap and Baseten's Elephant) judged each wording of the population, intervention, comparator and outcome against the others; two wordings count as the same when at least three of them agree and none clearly disagrees, and findings count as this question only when all four elements match. Timing, setting and subgroup never split a question: they are shown on each finding.