Question · asked by 1 review
Glass ionomer cement vs Amalgam restoration: Success rate at 1 year
- Population
- Patients needing retrograde filling in root canal therapy (orthograde root canal filling or retreatment not possible; periapical surgery used to save the tooth); no age or gender limitations
- Intervention
- Glass ionomer cement
- Comparison
- Amalgam restoration
- Outcome
- Success rate at 1 year
It is very uncertain whether glass ionomer cement changes success rate at 1 year compared with amalgam (very low certainty).
What each review found
Current versions onlyNo studies found 0 · 0%GRADE not reported 0 · 0%Very low 2 · 100%Low 0 · 0%Moderate 0 · 0%High 0 · 0%
| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Glass ionomer cement vs Amalgam Success rate at 1 year · Patients needing retrograde filling in root canal therapy (orthograde root canal filling or retreatment not possible;… · 1 year It is very uncertain whether glass ionomer cement changes success rate at 1 year compared with amalgam (very low certainty). Materials for retrograde filling in root canal therapy (2021) · Cochrane All outcomes | RR 0.98 [0.86, 1.12] | 1 study | Very low |
Glass ionomer cement vs Amalgam Success rate at 1 year · Patients needing retrograde filling in root canal therapy (orthograde root canal filling or retreatment not possible;… · 1 year It is very uncertain whether glass ionomer cement changes success rate at 1 year compared with amalgam (very low certainty). Materials for retrograde filling in root canal therapy (2016) · Cochrane All outcomesearlier version CD005517.PUB2, superseded | RR 0.98 [0.86, 1.12] | 1 study | Very low |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Materials for retrograde filling in root canal therapy 2021 · Cochrane All outcomes | 2021-04 | Not listed | Not assessed |
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.