Composite resin vs High-viscosity GIC and bulk-fill composite resins: Restoration failure
- Population
- Children with carious primary teeth
- Intervention
- Composite resin
- Comparison
- High-viscosity GIC and bulk-fill composite resins
- Outcome
- Restoration failure
No comparative effect estimate was reported; tabulated failure rates ranged from 0% to 2.9%.
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Composite resin vs High-viscosity GIC and bulk-fill composite resins Restoration failure · Children with carious primary teeth · 1 year, University, Class II No comparative effect estimate was reported; tabulated failure rates ranged from 0% to 2.9%. Clinical effectiveness of restorative materials for the restoration of carious primary teeth without pulp… (2022) All outcomes | Annual failure rate (%) HVGIC 2.9%; composite resin 0%; bulk-fill CR1 0%; bulk-fill CR2 0% | 1 study · 30 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 effectiveness of restorative materials for the restoration of carious primary teeth without pulp therapy: a systematic review. 2022 All outcomes | December 28, 2020 | 53 | Low / Low |
Related questions
The same intervention and outcome, asked in another population or against another comparator.
- Composite resin vs Amalgam restoration · Permanent posterior teeth (molars/premolars) receiving direct restoration · 1 review
- Composite resin vs Light-cured compomer (polyacid-modified composite resin) · Children with carious primary teeth after vital or non-vital pulp therapy · 1 review
- Composite resin vs HVGIC · Children with carious primary teeth · 1 review
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.
- Akman and Tosun 2020 · 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.