Question · asked by 1 review
Light vibrational forces (LVF) vs Conventional orthodontic appliance treatment: Improvement in occlusion (Peer Assessment Rating (PAR) or other validated scale)
- Population
- Adolescents and adults with malocclusion undergoing orthodontic treatment with fixed or removable appliances
- Intervention
- Light vibrational forces (LVF)
- Comparison
- Conventional orthodontic appliance treatment
- Outcome
- Improvement in occlusion (Peer Assessment Rating (PAR) or other validated scale)
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Light vibrational forces (LVF) vs Conventional orthodontic appliance treatment (placebo or no adjunct) Improvement in occlusion / orthodontic treatment occlusal outcome (Peer Assessment Rating, PAR) · Adolescents and adults with malocclusion undergoing orthodontic treatment with fixed or removable appliances Non‐surgical adjunctive interventions for accelerating tooth movement in patients undergoing orthodontic… (2023) · Cochrane All outcomes | Woodhouse 2015 (81 participants, 22 dropouts): PAR reduction intervention median 28.0 (IQR 21.0-38.0), control median 29.0 (IQR 24.0-31.0), placebo median 27.0 (IQR 20.0-35.0); percentage improvement medians 88.9 /… | 1 study · 59 participants | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Non‐surgical adjunctive interventions for accelerating tooth movement in patients undergoing orthodontic treatment 2023 · Cochrane All outcomes | 2022-09 | Not listed | Not assessed |
Related questions
The same intervention and outcome, asked in another population or against another comparator.
- Light vibrational forces (LVF) vs Conventional orthodontic appliance treatment · People who had received fixed appliance orthodontic treatment · 0 reviews
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.