SynthesisBMJ open2025
Risk prediction models for dental caries in children and adolescents: a systematic review and meta-analysis.
Synthesis in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Association between miR-21 polymorphism and the risk of primary dental caries in preschool children.Odontology · 2026Article
- Development of a nomogram for prediction of occlusal dentin caries in Norwegian children at risk: a 36-month follow-up study.BMC oral health · 2026Article
- Prognostic Clinical Predictive Models for Dental Caries Using Artificial Intelligence: Methodological Considerations.Caries research · 2026Review
- Who Gets Dental Caries? A Comprehensive Review.Dentistry journal · 2026Review
- State-transition and simulation-based modeling approaches for simulating the progression of dental caries: a scoping review.Frontiers in oral health · 2026Review
- Multicohort external validation ofFrontiers in cellular and infection microbiology · 2026Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aimed to systematically evaluate published predictive models for dental caries in children and adolescents.
designA systematic review and meta-analysis of observational studies. DATA SOURCES: Comprehensive searches were conducted in PubMed, Web of Science, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, Embase, China National Knowledge Infrastructure, Wanfang Database, China Science and Technology Journal Database (VIP) and SinoMed for relevant studies published up to 18 January 2024. The search focused on caries prediction models in children and adolescents. ELIGIBILITY CRITERIA: Eligible studies included observational research (cohort, case-control and cross-sectional designs) that developed risk prediction models for dental caries in children and adolescents aged ≤18 years. Each model was required to include a minimum of two predictors. Studies were excluded if they were not available in English or Chinese, primarily focused on oral microbiome modelling, or lacked essential details regarding study design, model construction or statistical analyses.
resultsA total of 11 studies were included in the review. All models demonstrated a high risk of bias, primarily due to inappropriate statistical methods and unclear applicability resulting from insufficiently detailed presentations of the models. Logistic regression, random forests and support vector machines were the most commonly employed methods. Frequently used predictors included fluoride toothpaste use and brushing frequency. Reported area under the curve (AUC) values ranged from 0.57 to 0.91. A combined predictive model incorporating six caries predictors achieved an AUC of 0.79 (95% CI: 0.73 to 0.84).
conclusionsSimplified predictive models for childhood caries showed moderate discriminatory performance but exhibited a high risk of bias, as assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Future research should adhere to PROBAST guidelines to minimise bias risk, focus on enhancing model quality, employ rigorous study designs and prioritise external validation to ensure reliable and generalisable clinical predictions. PROSPERO REGISTRATION NUMBER: CRD42024523284.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.