Question · asked by 1 review
Bruxism vs No bruxism: Oral health-related quality of life
- Population
- Adolescents (10-19 years)
- Intervention
- Bruxism
- Comparison
- No bruxism
- Outcome
- Oral health-related quality of life
Bruxism was not shown to impact OHRQoL in adolescents (narrative synthesis; no pooled estimate or GRADE rating).
What each review found
Include earlier review versionsNo studies found 0 · 0%GRADE not reported 2 · 100%Very low 0 · 0%Low 0 · 0%Moderate 0 · 0%High 0 · 0%
| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Bruxism vs No bruxism 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 Bruxism was not shown to impact OHRQoL in adolescents (narrative synthesis; no pooled estimate or GRADE rating). Oral-health-related quality of life in adolescents: umbrella review. (2023) All outcomes | While dental erosion and bruxism have not been shown to impact the quality of life of adolescents [26]. Bruxism does not impact quality of life in children and adolescentes [33]. | Not reported | GRADE not reported |
Bruxism vs No bruxism Oral health-related quality of life · Adolescents aged 10-19 years The umbrella review reported that bruxism has not been shown to affect oral health-related quality of life. Oral-health-related quality of life in adolescents: umbrella review. (2023) All outcomes | no estimate extracted | 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.
- Bruxism vs No bruxism · Indian children and adults · 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.