Question · asked by 1 review

Multivariable caries prediction model vs Additional prediction model(s): Coronal and/or root caries increment

Population
Individuals of all ages
Intervention
Multivariable caries prediction model
Comparison
Additional prediction model(s)
Outcome
Coronal and/or root caries increment

The review reported this model performance without meta-analysis.

What each review found

Current versions only
No studies found 0 · 0%GRADE not reported 2 · 100%Very low 0 · 0%Low 0 · 0%Moderate 0 · 0%High 0 · 0%
FindingEffectStudiesGRADE certainty
Multivariable caries prediction model vs Additional prediction model(s)
Coronal and/or root caries increment · Individuals of all ages · Follow-up ≥1 year, Oral health care without geographic restriction, Powell,1991 [21]; model performance 1
The review reported this model performance without meta-analysis.
Multivariable prediction models of caries increment: a systematic review and critical appraisal. (2023) All outcomes
LR+ 2.20 [0.74, 6.53]1 studyGRADE not reported
Multivariable caries prediction model vs Additional prediction model(s)
Coronal and/or root caries increment · Individuals of all ages · Follow-up ≥1 year, Oral health care without geographic restriction, Powell,1991 [21]; model performance 1
The review reported this model performance without meta-analysis.
Multivariable prediction models of caries increment: a systematic review and critical appraisal. (2023) All outcomes
LR− 0.20 [0.04, 0.90]1 studyGRADE not reported

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

The reviews

ReviewSearched up toIncluded studiesAMSTAR 2
Multivariable prediction models of caries increment: a systematic review and critical appraisal.
2023 All outcomes
23 April 202122Not 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.

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.