Composite resin with etch-and-rinse (total-etch) adhesion vs Composite resin with self-etch adhesion (same universal adhesive): Treatment failure (annual failure rate)
- Population
- Children with moderately deep dentine caries in primary molars, no pulp therapy
- Intervention
- Composite resin with etch-and-rinse (total-etch) adhesion
- Comparison
- Composite resin with self-etch adhesion (same universal adhesive)
- Outcome
- Treatment failure (annual failure rate)
Etch-and-rinse adhesion failed more than self-etch (19.5% vs 7.0% annual failure) in one RCT; low quality of evidence (ungraded).
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Composite resin with etch-and-rinse (total-etch) adhesion vs Composite resin with self-etch adhesion (same universal adhesive) Treatment failure (annual failure rate) · Children with moderately deep dentine caries in primary molars, no pulp therapy · 18 months (Lenzi 2017), University (Brazil), Adhesive strategy (etch-and-rinse vs self-etch) Etch-and-rinse adhesion failed more than self-etch (19.5% vs 7.0% annual failure) in one RCT; low quality of evidence (ungraded). Clinical effectiveness of restorative materials for the restoration of carious primary teeth without pulp… (2022) All outcomes | Annual failure rate (%) etch-and-rinse 19.5%; self-etch 7.0% | 1 study · 44 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 |
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.
- Ersin 2008 · 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.