Erbium:YAG laser vs Untreated / high-speed-handpiece control enamel: Enamel resistance to demineralization (microhardness / lesion depth)
Er:YAG laser showed a mixed and limited effect on enamel resistance to demineralization: feasible for controlling progression around restorations but not preventing caries formation, and ineffective in pits and fissures (ungraded, 2 ex-vivo studies, no pooled estimate).
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Er:YAG laser irradiation vs Untreated / high-speed-handpiece control enamel Enamel resistance to demineralization (microhardness / lesion depth) · Human enamel adjacent to restorations or in pits and fissures (in situ / ex vivo) · Ex-vivo/in situ (laboratory) Er:YAG laser showed a mixed and limited effect on enamel resistance to demineralization: feasible for controlling progression around restorations but not preventing caries formation, and ineffective in pits and fissures (ungraded, 2 ex-vivo studies, no pooled estimate). Clinical and ex-vivo effect of LASERs on prevention of early-enamel caries: systematic review & meta-analyses. (2024) All outcomes | Narrative only (not meta-analyzed). Colucci: Er:YAG laser feasible for controlling caries progression adjacent to restorations but not able to prevent caries formation (optimum 2 Hz). Correa-Afonso: Er:YAG did not… | 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 |
|---|---|---|---|
| Clinical and ex-vivo effect of LASERs on prevention of early-enamel caries: systematic review & meta-analyses. 2024 All outcomes | 2023-01-30 | 8 | Critically Low / Critically Low |
Studies behind the findings
The primary studies the full-text reviews tie to this outcome. Reviews with headline outcomes only do not list them here.
- Effect of Er:YAG laser on enamel demineralization around restorations. · 2015 · included by 1 of these 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.