Question · asked by 1 review
Laser fluorescence (LF; DIAGNOdent) vs Reference standard (histology, excavation, or enhanced visual examination): Diagnostic accuracy (sensitivity, specificity) - not pooled
- Population
- Permanent and primary teeth, smooth surfaces, with suspected caries lesions
- Intervention
- Laser fluorescence (LF; DIAGNOdent)
- Comparison
- Reference standard (histology, excavation, or enhanced visual examination)
- Outcome
- Diagnostic accuracy (sensitivity, specificity) - not pooled
Only 2 LF studies assessed smooth surfaces, so no pooled estimate was possible; both reported higher specificity than sensitivity (evidence not graded).
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Laser fluorescence (LF; DIAGNOdent) vs Reference standard (histological examination, operative intervention, or other/visual examination) Diagnostic accuracy (sensitivity, specificity) - not pooled · Permanent and primary teeth, smooth surfaces, with suspected caries lesions · Initial and more advanced caries lesions thresholds, Permanent and primary teeth; smooth surfaces Only 2 LF studies assessed smooth surfaces, so no pooled estimate was possible; both reported higher specificity than sensitivity (evidence not graded). Fluorescence-based methods for detecting caries lesions: systematic review, meta-analysis and sources of… (2013) All outcomes | Only two included studies assessed smooth surfaces (both used LF; one permanent, one primary); no meta-analysis was possible. Both reported specificity higher than sensitivity at both thresholds. | 2 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.