Composite resin vs Light-cured compomer (polyacid-modified composite resin): Restoration failure
- Population
- Children with carious primary teeth after vital or non-vital pulp therapy
- Intervention
- Composite resin
- Comparison
- Light-cured compomer (polyacid-modified composite resin)
- Outcome
- Restoration failure
In one RCT, composite resin had an annual failure rate of 0% after 2 years.
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Composite resin vs Compomer Restoration failure · Children with carious primary teeth after vital or non-vital pulp therapy · 2 years, Not specified, Turkey In one RCT, composite resin had an annual failure rate of 0% after 2 years. Clinical effectiveness of restorative materials for the restoration of carious lesions in pulp treated… (2022) All outcomes | Annual failure rate (AFR, %) 0 | 1 study · 84 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 lesions in pulp treated primary teeth: a systematic review. 2022 All outcomes | December 28, 2020 | 5 | 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 High-viscosity GIC and bulk-fill composite resins · Children with carious primary teeth · 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.
- Cehreli et al. 2006 · 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.