Question · asked by 1 review
Chin cup (600 g force) vs Chin cup (300 g force): ANB
- Population
- Children and adolescents (aged 16 years or younger) with prominent lower front teeth (Class III malocclusion)
- Intervention
- Chin cup (600 g force)
- Comparison
- Chin cup (300 g force)
- Outcome
- ANB
Chin cup (600 g force) showed no clear difference in ANB compared with chin cup (300 g force); GRADE not reported.
What each review found
Current versions onlyNo 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 |
|---|---|---|---|
Chin cup (600 g force) vs Chin cup (300 g force) ANB · Children and adolescents (aged 16 years or younger) with prominent lower front teeth (Class III malocclusion) · End of treatment Chin cup (600 g force) showed no clear difference in ANB compared with chin cup (300 g force); GRADE not reported. Orthodontic treatment for prominent lower front teeth (Class III malocclusion) in children (2024) · Cochrane All outcomes | MD 0.10 [-0.31, 0.51] | 1 study · 40 participants | GRADE not reported |
Chin cup (600 g force) vs Chin cup (300 g force) ANB · Children and adolescents (aged 16 years or younger) with prominent lower front teeth (Class III malocclusion) · End of treatment Chin cup (600 g force) showed no clear difference in ANB compared with chin cup (300 g force); GRADE not reported. Orthodontic treatment for prominent lower front teeth (Class III malocclusion) in children (2013) · Cochrane All outcomesearlier version CD003451.PUB2, superseded | MD 0.10 [-0.31, 0.51] | 1 study | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Orthodontic treatment for prominent lower front teeth (Class III malocclusion) in children 2024 · Cochrane All outcomes | 2023-01 | 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.