Question · asked by 1 review
Quantitative light-induced fluorescence (QLF) vs Reference standard (histology, excavation, or enhanced visual examination): Diagnostic accuracy (sensitivity, specificity) - not pooled
- Population
- Permanent teeth (occlusal and smooth surfaces), with suspected caries lesions
- Intervention
- Quantitative light-induced fluorescence (QLF)
- Comparison
- Reference standard (histology, excavation, or enhanced visual examination)
- Outcome
- Diagnostic accuracy (sensitivity, specificity) - not pooled
Only 3 QLF studies were included, so no pooled estimate of QLF diagnostic accuracy could be calculated (evidence not graded).
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Quantitative light-induced fluorescence (QLF) vs Reference standard (histological examination, operative intervention, or other/visual examination) Diagnostic accuracy (sensitivity, specificity, DOR) - not pooled · Permanent teeth (occlusal and smooth surfaces), with suspected caries lesions · Initial and more advanced caries lesions thresholds, Permanent teeth; laboratory and clinical Only 3 QLF studies were included, so no pooled estimate of QLF diagnostic accuracy could be calculated (evidence not graded). Fluorescence-based methods for detecting caries lesions: systematic review, meta-analysis and sources of… (2013) All outcomes | Only three articles using QLF were included (all permanent teeth); no meta-analysis could be performed. Individual studies reported high sensitivity or high specificity depending on threshold and surface. | 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 |
|---|---|---|---|
| Fluorescence-based methods for detecting caries lesions: systematic review, meta-analysis and sources of heterogeneity. 2013 All outcomes | 19 June 2012 | 75 | Not applicable |
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.