Question · asked by 1 review

Multivariable caries prediction model vs Alternative prediction model(s) within the same study, or no formal comparator (models compared descriptively; no between-model statistical test): Predictive performance for coronal caries increment (negative likelihood ratio, LR-)

Population: Children and adolescents (aged 2-19) at risk of coronal caries increment (caries onset or progression)
Intervention: Multivariable caries prediction model
Comparison: Alternative prediction model(s) within the same study, or no formal comparator (models compared descriptively; no between-model statistical test)
Outcome: Predictive performance for coronal caries increment (negative likelihood ratio, LR-)

Some developmental coronal-caries models achieved useful negative likelihood ratios (LR-<=0.20), but performance varied widely; no pooled estimate; ungraded.

What each review found

Include earlier review versions

No studies found 0 · 0%GRADE not reported 2 · 100%Very low 0 · 0%Low 0 · 0%Moderate 0 · 0%High 0 · 0%

Finding	Effect	Studies	GRADE certainty
Multivariable prediction model of caries increment (>=3 predictors), e.g. logistic-regression or algorithm-based caries-risk models vs Alternative prediction model(s) within the same study, or no formal comparator (models compared descriptively; no between-model statistical test) Predictive performance for coronal caries increment (negative likelihood ratio, LR-) · Children and adolescents (aged 2-19) at risk of coronal caries increment (caries onset or progression) · Follow-up >=1 year (included studies 1-5 years), Oral health care; children/adolescents; no geographical restriction, Model development studies Some developmental coronal-caries models achieved useful negative likelihood ratios (LR-<=0.20), but performance varied widely; no pooled estimate; ungraded. Multivariable prediction models of caries increment: a systematic review and critical appraisal. (2023) All outcomes	No meta-analysis. Per-model LR- (Table 2) for the 8 development studies ranged roughly 0.11-0.94 (e.g. Angulo 0.94, Demers 0.14-0.40, Gao 2010 0.11-0.30). Overall across all 66 models LR- ranged 0.0-1.1; LR-<=0.20…	8 studies	GRADE not reported
Multivariable prediction model of caries increment (>=3 predictors), e.g. logistic-regression or algorithm-based caries-risk models vs Alternative prediction model(s) within the same study, or no formal comparator (models compared descriptively; no between-model statistical test) Predictive performance for coronal caries increment (negative likelihood ratio, LR-) · Children and adolescents (aged 2-19) at risk of coronal caries increment (caries onset or progression) · Follow-up >=1 year (included studies 1-5 years), Oral health care; children/adolescents; no geographical restriction, Model validation studies Externally validated coronal-caries models had widely varying negative likelihood ratios, several above the useful rule-out threshold; no pooled estimate; ungraded. Multivariable prediction models of caries increment: a systematic review and critical appraisal. (2023) All outcomes	No meta-analysis. Per-model LR- (Table 2) across the 9 validation studies ranged from about 0.14 to 1.13 (e.g. Gao 2013 NUS-CRA 0.26, CAMBRA 0.14-0.31, CAT up to ~1.10; Campus 0.20; Pang 2020 low-risk 1.13). Overall…	9 studies	GRADE not reported

Findings are shown side by side, never pooled across reviews.

The reviews

Review	Searched up to	Included studies	AMSTAR 2
Multivariable prediction models of caries increment: a systematic review and critical appraisal. 2023 All outcomes	23 April 2021	22	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.

A New Model for Caries Risk Prediction in Teenagers Using a Machine Learning Algorithm Based on Environmental and Genetic Factors. · 2021 · included by 1 of these review
A methodological study to assess the measurement properties (reliability and validity) of a caries risk assessment tool for young children. · 2020 · included by 1 of these review
Validation of Cariogram in Caries Prediction in Women and Their Children 4 Years After Pregnancy - Longitudinal Study. · 2020 · included by 1 of these review
Validation of different Cariogram settings and factor combinations in preschool children from areas with high caries risk. · 2019 · included by 1 of these review
Caries risk assessment in young adults: a 3 year validation of the Cariogram model. · 2015 · included by 1 of these review
Validity of caries risk assessment programmes in preschool children. · 2013 · included by 1 of these review
Cariogram validity in schoolchildren: a two-year follow-up study. · 2012 · included by 1 of these review
Risk factors of caries progression in a Hispanic school-aged population. · 2011 · included by 1 of these review
Building caries risk assessment models for children. · 2010 · included by 1 of these review
Caries risk profiles in schoolchildren over 2 years assessed by Cariogram. · 2010 · included by 1 of these review
Validation of an age-modified caries risk assessment program (Cariogram) in preschool children. · 2009 · included by 1 of these review
Clinical, salivary, and bacterial markers for caries risk assessment in schoolchildren: a 4-year follow-up. · 2009 · included by 1 of these review
Evaluation of a computer program for caries risk assessment in schoolchildren. · 2002 · included by 1 of these review
Caries incidence, effect of preventive measures, and caries prediction in Uruguayan children. · 1995 · included by 1 of these review
A multivariate model to predict caries increment in Montreal children aged 5 years. · 1992 · included by 1 of these review
University of North Carolina Caries Risk Assessment Study: comparisons of high risk prediction, any risk prediction, and any risk… · 1992 · included by 1 of these review
The University of North Carolina Caries Risk Assessment study: further developments in caries risk prediction. · 1992 · 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.