Question · asked by 1 review

Quantitative light-induced fluorescence (QLF) vs Reference standard (bitewing radiography, ADA classification, ICDAS II): Proximal caries detection: sensitivity

Population
Patients (in vivo); permanent and primary teeth, proximal (approximal) surfaces
Intervention
Quantitative light-induced fluorescence (QLF)
Comparison
Reference standard (bitewing radiography, ADA classification, ICDAS II)
Outcome
Proximal caries detection: sensitivity

In vivo, QLF pooled sensitivity for proximal caries detection was 0.74 (4 studies; ungraded).

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
Quantitative light-induced fluorescence (QLF) vs Reference standard (bitewing radiography, ADA classification, ICDAS II)
Proximal caries detection: sensitivity · Patients (in vivo); permanent and primary teeth, proximal (approximal) surfaces · In vivo (clinical), Overall (all dentition)
In vivo, QLF pooled sensitivity for proximal caries detection was 0.74 (4 studies; ungraded).
Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) All outcomes
Sensitivity 0.744 studiesGRADE not reported
Quantitative light-induced fluorescence (QLF) vs Reference standard (bitewing radiography, ADA classification, ICDAS II)
Proximal caries detection: sensitivity · Patients (in vivo); permanent and primary teeth, proximal (approximal) surfaces · In vivo (clinical), Permanent dentition
In vivo, QLF pooled sensitivity for proximal caries in permanent teeth was 0.76 (3 studies; ungraded).
Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) All outcomes
Sensitivity 0.763 studiesGRADE not reported

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

The reviews

The same intervention and outcome, asked in another population or against another comparator.

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.