Non-Cochrane · prognostic · PM40044209
Risk prediction models for dental caries in children and adolescents: a systematic review and meta-analysis.
Wang Xijia, Zhang Peng, Lu Huifei, et al. · BMJ open · 2025 · PubMed · doi:10.1136/bmjopen-2024-088253
Full textEvery question was extracted from the full text, figures and supplements by two independent LLM extractors, disagreements adjudicated. Rows cite where in the review the value comes from.
Questions
7
current version
GRADE rated
0
No studies
0
Included studies
16
linked below
No studies 0 · 0%Ungraded 7 · 100%Very low 0 · 0%Low 0 · 0%Moderate 0 · 0%High 0 · 0%
Ungraded means an effect was reported without a GRADE rating; it is not a gap. No studies means the review found nothing to answer the question. The six categories are a display order, not a score.
Review question
| Element | Planned (methods) | Found (results) |
|---|---|---|
| Population | Children and adolescents aged 1-18 years | 11 studies (n=11); children/adolescents aged 1-18 years; primary dentition (n=5), young permanent teeth (n=5), both (n=1); China (n=5), Korea (n=2), Brazil (n=2), Singapore (n=1), Sri Lanka (n=1); sample sizes 200-4573 |
| Intervention | Developed/published simplified caries prediction models with >=2 predictors (excluding oral-microbiome/microbiological models) | 25 simplified caries prediction models; methods: logistic regression (n=10), random forest (n=4), decision tree (n=3), XGBoost (n=3), SVM (n=1), MLP neural network (n=1), AdaBoost (n=1), kNN (n=1); candidate predictors 9-39 |
| Comparison | Models without competing models (no comparator model) | No competing-model comparisons (single-model development/validation) |
| Outcomes | Prediction of dental caries in children (simplified prediction models; discrimination and calibration) | Model discrimination (AUC/C-statistic) ranged 0.566-0.931; pooled AUC of 6 models 0.79 (95% CI 0.73-0.84), I2=97.3%; calibration assessed variously (calibration curves, Hosmer-Lemeshow, Brier score, DCA); all studies high risk of bias (PROBAST) |
| Study designs | Home/community preventive oral healthcare; detection of undiagnosed caries in asymptomatic children | School, community and clinical/medical settings (varied across studies) |
| Timing | For asymptomatic children; rapid detection of undiagnosed caries (prediction horizon) | Cohort follow-up 3 months to 10 years; primary and young permanent dentition |
Search date: 18 January 2024. Databases: PubMed; Web of Science; Cochrane Library; CINAHL; Embase; China National Knowledge Infrastructure (CNKI); Wanfang Database; China Science and Technology Journal Database (VIP); SinoMed
Questions and results
Simple childhood caries prediction models with at least two predictors vs No competing model
Population: Children and adolescents (≤18 years)
| Outcome | Effect | Evidence | Status / GRADE |
|---|---|---|---|
Dental caries discrimination (area under the curve) Validation assessment; prediction horizon not reported The Duan (2021) model had an AUC of 0.71 (95% CI 0.69 to 0.72). Source: Figure 2 · same question in other reviews | AUC 0.71 [0.69, 0.72] | 1 study | Ungraded |
Dental caries discrimination (area under the curve) Validation assessment; prediction horizon not reported The Luo (2021) model had an AUC of 0.77 (95% CI 0.76 to 0.77). Source: Figure 2 · same question in other reviews | AUC 0.766 [0.761, 0.771] | 1 study | Ungraded |
Model discrimination (area under the ROC curve, AUC) Prediction of future/undiagnosed caries; cohort follow-up 3 months-10 years · Overall (6 validated models pooled) Population: Children and adolescents aged 1-18 years 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. Source: Figure 2 (forest plot, overall diamond); Results 'Statistical methods of models'; Abstract; Conclusion · same question in other reviews | AUC 0.79 [0.73, 0.84] | 6 studies | Ungraded |
Dental caries discrimination (area under the curve) Validation assessment; prediction horizon not reported · Yaxuan (2023) The Liu (2023) model had an AUC of 0.93 (95% CI 0.88 to 0.97). Source: Figure 2 · same question in other reviews | AUC 0.931 [0.876, 0.967] | 1 study | Ungraded |
Dental caries discrimination (area under the curve) Validation assessment; prediction horizon not reported · Wickramasinghe (2022) The Danushi (2022) model had an AUC of 0.59 (95% CI 0.51 to 0.67). Source: Figure 2 · same question in other reviews | AUC 0.592 [0.514, 0.671] | 1 study | Ungraded |
Dental caries discrimination (area under the curve) Validation assessment; prediction horizon not reported The Qu (2022) model had an AUC of 0.91 (95% CI 0.87 to 0.94). Source: Figure 2 · same question in other reviews | AUC 0.91 [0.87, 0.94] | 1 study | Ungraded |
Dental caries discrimination (area under the curve) Validation assessment; prediction horizon not reported · Kim (2018) The Kim (2018) model had an AUC of 0.78 (95% CI 0.68 to 0.88). Source: Figure 2 · same question in other reviews | AUC 0.782 [0.681, 0.884] | 1 study | Ungraded |
Included primary studies 16
Open as a trial list| Study | Also included by | Outcomes it informs here | Trial PICO | Results |
|---|---|---|---|---|
| Kalhan (2020) Caries Risk Prediction Models in a Medical Health Care Setting. PMID:32311276 | 1 other review | not stated | not extracted yet | not extracted yet |
| Duan 2021 A predictive nomogram: a cross-sectional study on a simple-to-use model for screening 12-year-old children for severe caries in middle… PMID:34544417 | this review only | Dental caries discrimination (area under the curve) | not extracted yet | not extracted yet |
| Kalhan 2020 DOI:10.1177/0022034520913032 | this review only | not stated | not extracted yet | not extracted yet |
| Kim (2018) Simplified Prediction Model for Accurate Assessment of Dental Caries Risk among Participants Aged 10-18 Years. PMID:30333362 | this review only | Dental caries discrimination (area under the curve) | not extracted yet | not extracted yet |
| Kim 2018 DOI:10.1620/tjem.246.8 | this review only | Dental caries discrimination (area under the curve) | not extracted yet | not extracted yet |
| Luo 2021 Nomogram prediction of caries risk among schoolchildren age 7 years based on a cohort study in Shanghai. PMID:34851774 | this review only | Dental caries discrimination (area under the curve) | not extracted yet | not extracted yet |
| Montenegro 2008 A Comparative Study of Machine Learning Techniques for Caries Prediction DOI:10.1109/ictai.2008.138 | this review only | not stated | not extracted yet | not extracted yet |
| Park 2021 Prediction Models of Early Childhood Caries Based on Machine Learning Algorithms. PMID:34444368 | this review only | not stated | not extracted yet | not extracted yet |
| Qu (2022) Prediction model for early childhood caries risk based on behavioral determinants using a machine learning algorithm. PMID:36384058 | this review only | Dental caries discrimination (area under the curve) | not extracted yet | not extracted yet |
| Qu 2022 Prediction model for early childhood caries risk based on behavioral determinants using a machine learning algorithm DOI:10.1016/j.cmpb.2022.107221 | this review only | Dental caries discrimination (area under the curve) | not extracted yet | not extracted yet |
| Toledo Reyes (2023) Early Childhood Predictors for Dental Caries: A Machine Learning Approach. PMID:37246832 | this review only | not stated | not extracted yet | not extracted yet |
| Toledo Reyes 2023 DOI:10.1177/00220345231170537 | this review only | not stated | not extracted yet | not extracted yet |
| Wickramasinghe (2022) Identifying high-risk children for dental caries in school settings: A simple predictive model. PMID:38421295 | this review only | not stated | not extracted yet | not extracted yet |
| Wickramasinghe 2022 Identifying high-risk children for dental caries in school settings: A simple predictive model DOI:10.4038/cmj.v67i4.9744 | this review only | Dental caries discrimination (area under the curve) | not extracted yet | not extracted yet |
| Xinmiao 2023 Prediction model of dental caries in 12-year-old children in Sichuan Province based on machine learning. PMID:38597034 | this review only | not stated | not extracted yet | not extracted yet |
| Yaxuan 2023 25-Hydroxyvitamin D3 metabolism in a human leukemia cell line. PMID:3102023 | this review only | Dental caries discrimination (area under the curve) | not extracted yet | not extracted yet |
Overlap with other reviews
Reviews that include the same primary studies are not independent evidence. Shared counts match studies by PMID, DOI or NCT, and otherwise by author and year (a probable match).
| Review | Shared studies | Jaccard |
|---|---|---|
| Maternal smoking during pregnancy and early childhood dental caries in children: a systematic review and meta-analysis. (2024) | 1 | 0.04 |