Cariogram multivariable caries-risk model (including modified/reduced-predictor versions) vs Alternative prediction model(s) within the same study, or no formal comparator (models compared descriptively; no between-model statistical test): Predictive performance of the Cariogram for caries increment (negative likelihood ratio, LR-)
Modified Cariogram models showed negative likelihood ratios of about 0.5-0.61, above the useful rule-out threshold; no pooled estimate; ungraded.
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Cariogram multivariable caries-risk model (including modified/reduced-predictor versions) vs Alternative prediction model(s) within the same study, or no formal comparator (models compared descriptively; no between-model statistical test) Predictive performance of the Cariogram for caries increment (negative likelihood ratio, LR-) · Children/adolescents (coronal caries) and older adults (root caries); populations from areas with varying caries risk · Follow-up >=1 year (included studies 1-5 years), Oral health care; children/adolescents and older adults; multiple countries, Model validation studies Modified Cariogram models showed negative likelihood ratios of about 0.5-0.61, 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 | Review-stated LR- range 0.5-0.61 across the Cariogram validation models (with one exception). Model performance was largely unaffected by removing the predictors mutans streptococci in saliva, saliva secretion and… | 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 |
|---|---|---|---|
| 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
None of these reviews ties its included studies to this outcome in the data here. Cochrane reviews and reviews with headline outcomes only do not list their studies yet.
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.