Composite resin vs Compomer, glass-ionomer/RMGIC, amalgam, or glass carbomer: Treatment failure (annual failure rate)
- Population
- Children with previously unrestored, vital carious lesions in primary teeth (no pulp therapy); aged 1.5-11 years
- Intervention
- Composite resin
- Comparison
- Compomer, glass-ionomer/RMGIC, amalgam, or glass carbomer
- Outcome
- Treatment failure (annual failure rate)
Composite resin (hybrid 0-19.5%; bulk-fill 0-16.9%) performed comparably to or better than most alternatives across 11 RCTs; evidence is of low to unclear quality (ungraded).
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Composite resin restoration (hybrid or bulk-fill) vs Compomer, glass-ionomer/RMGIC, amalgam, or glass carbomer Treatment failure (annual failure rate) · Children with previously unrestored, vital carious lesions in primary teeth (no pulp therapy); aged 1.5-11 years · 12-48 months, Mostly university settings; 1 private dental practice Composite resin (hybrid 0-19.5%; bulk-fill 0-16.9%) performed comparably to or better than most alternatives across 11 RCTs; evidence is of low to unclear quality (ungraded). Clinical effectiveness of restorative materials for the restoration of carious primary teeth without pulp… (2022) All outcomes | Annual failure rate (%) hybrid 0-19.5%; bulk-fill 0-16.9% | 11 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 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 |
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.
- Dutta et al. 2001 · 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.