Fixed bonded Hyrax appliance followed by U-bow activator therapy vs no treatment: Canine expansion
- Population
- Children and adults with a posterior crossbite without a Class III skeletal relationship, cleft lip or palate, or other syndrome associated with craniofacial anomalies (all included studies were children aged 5 to 16 years)
- Intervention
- Fixed bonded Hyrax appliance followed by U-bow activator therapy
- Comparison
- no treatment
- Outcome
- Canine expansion
It is very uncertain whether fixed bonded Hyrax appliance followed by U-bow activator therapy changes canine expansion compared with no treatment (very low certainty).
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Fixed bonded Hyrax appliance followed by U-bow activator therapy vs No treatment Canine expansion · Children and adults with a posterior crossbite without a Class III skeletal relationship, cleft lip or palate, or… · 12 months after start of treatment It is very uncertain whether fixed bonded Hyrax appliance followed by U-bow activator therapy changes canine expansion compared with no treatment (very low certainty). Orthodontic treatment for posterior crossbites (2014) · Cochrane All outcomesearlier version CD000979.PUB2, superseded | U-bow activator 3.6 mm vs no treatment 1 mm; P<0.001 (no SD reported); statistically significant in favour of treatment | 1 study · 66 participants | Very low |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Orthodontic treatment for posterior crossbites 2021 · Cochrane All outcomes | 2021-04 | Not listed | Not assessed |
Related questions
The same intervention and outcome, asked in another population or against another comparator.
- Fixed bonded Hyrax appliance followed by U-bow activator therapy vs no treatment · Children and adults with a posterior crossbite, without a Class III skeletal… · 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.