Completed orthodontic treatment vs Untreated / never received orthodontic treatment: Oral health-related quality of life
- Population
- Adolescents (10-19 years)
- Intervention
- Completed orthodontic treatment
- Comparison
- Untreated / never received orthodontic treatment
- Outcome
- Oral health-related quality of life
Completing orthodontic treatment is associated with improved OHRQoL in adolescents (narrative synthesis; included reviews report low-to-moderate certainty; no pooled estimate or GRADE rating by the umbrella review).
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Completion of orthodontic treatment vs Untreated / never received orthodontic treatment Oral health-related quality of life (OHRQoL) · Adolescents (10-19 years) · Community/school/clinic settings; adolescents 10-19 years; included reviews conducted in multiple countries Completing orthodontic treatment is associated with improved OHRQoL in adolescents (narrative synthesis; included reviews report low-to-moderate certainty; no pooled estimate or GRADE rating by the umbrella review). Oral-health-related quality of life in adolescents: umbrella review. (2023) All outcomes | Adolescents who had never received orthodontic treatment had a greater impact on quality of life compared to patients who had already completed treatment [34, 40, 44-46]. Orthodontic treatment during childhood or… | 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 |
|---|---|---|---|
| Oral-health-related quality of life in adolescents: umbrella review. 2023 All outcomes | January 2022 | 22 | Not applicable |
Related questions
The same intervention and outcome, asked in another population or against another comparator.
- Completed orthodontic treatment vs No previous or incomplete orthodontic treatment · Adolescents (10-19 years) · 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.