Question · asked by 1 review
Atraumatic restorative treatment with high-viscosity GIC (class I) vs Amalgam restoration: Success or failure of restoration (as defined by the USPHS criteria)
- Population
- Children with carious primary teeth
- Intervention
- Atraumatic restorative treatment with high-viscosity GIC (class I)
- Comparison
- Amalgam restoration
- Outcome
- Success or failure of restoration (as defined by the USPHS criteria)
ART with high-viscosity GIC and amalgam had similar class I restoration success at 24 months (ungraded).
What each review found
Current versions onlyNo studies found 0 · 0%GRADE not reported 2 · 100%Very low 0 · 0%Low 0 · 0%Moderate 0 · 0%High 0 · 0%
| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Atraumatic restorative treatment with high-viscosity GIC (class I) vs Amalgam restoration Restoration success/failure · Children (5-7 years) with caries in primary teeth · 24 months ART with high-viscosity GIC and amalgam had similar class I restoration success at 24 months (ungraded). Minimal intervention dentistry for managing carious lesions into dentine in primary teeth: an umbrella review. (2022) All outcomes | RR 1.07 [0.91, 1.27] | 3 studies | GRADE not reported |
Atraumatic restorative treatment with high-viscosity GIC (class I) vs Amalgam restoration Restoration success/failure · Children (5-7 years) with caries in primary teeth · 12 months ART with high-viscosity GIC and amalgam had similar class I restoration success at 12 months (ungraded). Minimal intervention dentistry for managing carious lesions into dentine in primary teeth: an umbrella review. (2022) All outcomes | RR 0.93 [0.83, 1.06] | 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 |
|---|---|---|---|
| Minimal intervention dentistry for managing carious lesions into dentine in primary teeth: an umbrella review. 2022 All outcomes | August 2020 | 35 | Low / Low |
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.