CO2 laser (carbon dioxide) vs No anticaries intervention (untreated enamel): Enamel surface / cross-sectional microhardness
CO2 laser irradiation changes enamel microhardness versus untreated enamel (MD 49.55, CI 37.74 to 61.37; ungraded, 3 ex-vivo studies, I2=48%); the forest plot shows higher microhardness in laser-treated enamel, although the review text states the opposite.
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
CO2 laser irradiation (0.4 to 5 W) vs Untreated control enamel Enamel surface / cross-sectional microhardness · Human / bovine enamel specimens under cariogenic challenge (in situ / ex vivo) · Ex-vivo/in situ (laboratory; intraoral appliances with enamel slabs) CO2 laser irradiation changes enamel microhardness versus untreated enamel (MD 49.55, CI 37.74 to 61.37; ungraded, 3 ex-vivo studies, I2=48%); the forest plot shows higher microhardness in laser-treated enamel, although the review text states the opposite. Clinical and ex-vivo effect of LASERs on prevention of early-enamel caries: systematic review & meta-analyses. (2024) All outcomes | MD 49.55 [37.74, 61.37] | 3 studies · 64 participants | 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.
- Assessment of Inhibition of Mineral Loss from Human Tooth Enamel by Carbon Dioxide Laser and 1.23% Acidulated Phosphate Fluoride. · 2019 · included by 1 of these review
- In situ study of the anticariogenic potential of fluoride varnish combined with CO2 laser on enamel. · 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.