Epilepsy (as exposure/condition) vs Healthy participants without epilepsy (control group): Mean DMFT (decayed, missing, filled permanent teeth)
- Population
- Children and adolescents with epilepsy
- Intervention
- Epilepsy (as exposure/condition)
- Comparison
- Healthy participants without epilepsy (control group)
- Outcome
- Mean DMFT (decayed, missing, filled permanent teeth)
In children/adolescents, the higher DMFT with epilepsy is not statistically significant (SMD 0.334, 95% CI -0.140 to 0.827, P = .164; ungraded subgroup).
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Epilepsy (as exposure/condition) vs Healthy participants without epilepsy (control group) DMFT (permanent teeth) · Children and adolescents with epilepsy · Clinical/community dental examination; multiple countries (England, Turkey, Egypt, Iran, China, India), Children/adolescents In children/adolescents, the higher DMFT with epilepsy is not statistically significant (SMD 0.334, 95% CI -0.140 to 0.827, P = .164; ungraded subgroup). Evaluating the DMFT and dmft indices in people with epilepsy: A systematic review and meta-analysis. (2025) All outcomes | SMD 0.334 [-0.140, 0.827] | Not reported | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Evaluating the DMFT and dmft indices in people with epilepsy: A systematic review and meta-analysis. 2025 All outcomes | Up to October 30, 2024 | 12 | Not applicable |
Related questions
The same intervention and outcome, asked in another population or against another comparator.
- Epilepsy (as exposure/condition) vs Healthy participants without epilepsy (control group) · people with epilepsy · 1 review
- Epilepsy (as exposure/condition) vs Healthy participants without epilepsy (control group) · Adults with epilepsy · 1 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.