Fibre-optic transillumination (FOTI) vs Histological validation (in vitro) or direct visual examination after tooth separation / bioptical cavity preparation (in vivo): detection of proximal caries
- Population
- Permanent posterior teeth, proximal surfaces (in vitro extracted teeth and in vivo patients)
- Intervention
- Fibre-optic transillumination (FOTI)
- Comparison
- Histological validation (in vitro) or direct visual examination after tooth separation / bioptical cavity preparation (in vivo)
- Outcome
- detection of proximal caries
For fibre-optic transillumination (FOTI), studies were identified (in vitro N=4, in vivo N=3) but none had sufficiently low risk of bias and adequate reporting to be pooled; no meta-analytic estimate could be derived.
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Fibre-optic transillumination (FOTI) vs Histological validation (in vitro) or direct visual examination after tooth separation / bioptical cavity preparation (in vivo) Diagnostic accuracy for proximal caries detection · Permanent posterior teeth, proximal surfaces (in vitro extracted teeth and in vivo patients) · In vitro and in vivo; permanent posterior teeth, proximal surfaces For fibre-optic transillumination (FOTI), studies were identified (in vitro N=4, in vivo N=3) but none had sufficiently low risk of bias and adequate reporting to be pooled; no meta-analytic estimate could be derived. Systematic review and meta-analysis of diagnostic studies of proximal surface caries. (2021) All outcomes | no estimate extracted | 0 studies | No studies found |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Systematic review and meta-analysis of diagnostic studies of proximal surface caries. 2021 All outcomes | Up to 31 December 2018 (English-language publications) | 36 | 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.