Quantitative light-induced fluorescence (QLF) vs Reference standard (histology; some ICDAS/radiography): Proximal caries detection: specificity
- Population
- Extracted human teeth (in vitro); permanent teeth, proximal (approximal) surfaces
- Intervention
- Quantitative light-induced fluorescence (QLF)
- Comparison
- Reference standard (histology; some ICDAS/radiography)
- Outcome
- Proximal caries detection: specificity
In vitro, QLF pooled specificity for proximal caries detection was 0.74 (3 permanent-teeth studies; no primary-dentition data; ungraded).
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Quantitative light-induced fluorescence (QLF) vs Reference standard (histology, radiography) Proximal caries detection: specificity · Extracted human teeth (in vitro); permanent teeth, proximal (approximal) surfaces · In vitro (laboratory), Overall (all studies permanent) In vitro, QLF pooled specificity for proximal caries detection was 0.74 (3 permanent-teeth studies; no primary-dentition data; ungraded). Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) All outcomes | Specificity 0.74 | 3 studies | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and locations: a systematic review and… 2025 All outcomes | May 2023 | 28 | Not applicable |
Related questions
The same intervention and outcome, asked in another population or against another comparator.
- Quantitative light-induced fluorescence (QLF) vs Reference standard (bitewing radiography, ADA classification, ICDAS II) · Patients (in vivo); permanent and primary teeth, proximal (approximal) surfaces · 1 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.