Light vibrational forces (LVF) vs Conventional orthodontic appliance treatment: Harms: non-serious adverse effects (headache, discomfort, loosening of temporary anchorage devices)
- 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
- Harms: non-serious adverse effects (headache, discomfort, loosening of temporary anchorage devices)
Light vibrational forces (LVF) showed no clear difference in harms: non-serious adverse effects (headache, discomfort, loosening… compared with conventional orthodontic appliance treatment (placebo or no adjunct); GRADE not reported.
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Light vibrational forces (LVF) vs Conventional orthodontic appliance treatment (placebo or no adjunct) Harms: non-serious adverse effects (headache, discomfort, loosening of temporary anchorage devices) · Adolescents and adults with malocclusion undergoing orthodontic treatment with fixed or removable appliances Light vibrational forces (LVF) showed no clear difference in harms: non-serious adverse effects (headache, discomfort, loosening… compared with conventional orthodontic appliance treatment (placebo or no adjunct); GRADE not reported. Non‐surgical adjunctive interventions for accelerating tooth movement in patients undergoing orthodontic… (2023) · Cochrane All outcomes | RR 0.96 [0.32, 2.85] | 1 study · 45 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 |
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.