Light-emitting diode (LED) photobiomodulation vs Conventional orthodontic appliance treatment: Rate of orthodontic tooth movement during space closure stage (maxillary canine distalisation), mm/day
- Population
- Adolescents and adults with malocclusion undergoing fixed orthodontic appliance treatment
- Intervention
- Light-emitting diode (LED) photobiomodulation
- Comparison
- Conventional orthodontic appliance treatment
- Outcome
- Rate of orthodontic tooth movement during space closure stage (maxillary canine distalisation), mm/day
It is very uncertain whether light-emitting diode (LED) photobiomodulation changes rate of orthodontic tooth movement during space closure stage… compared with conventional orthodontic appliance treatment (very low certainty).
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Light-emitting diode (LED) photobiomodulation vs Conventional orthodontic appliance treatment Rate of orthodontic tooth movement during space closure stage (maxillary canine distalisation), mm/day · Adolescents and adults with malocclusion undergoing fixed orthodontic appliance treatment · Space closure (canine retraction) It is very uncertain whether light-emitting diode (LED) photobiomodulation changes rate of orthodontic tooth movement during space closure stage… compared with conventional orthodontic appliance treatment (very low certainty). Non‐surgical adjunctive interventions for accelerating tooth movement in patients undergoing orthodontic… (2023) · Cochrane All outcomes | MD 0.006 [0, 0.02] | 1 study · 39 participants | Very low |
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.