Question · asked by 0 reviews

Light vibrational forces (LVF) vs Conventional orthodontic appliance treatment: Discomfort/pain

Population
People who had received fixed appliance orthodontic treatment
Intervention
Light vibrational forces (LVF)
Comparison
Conventional orthodontic appliance treatment
Outcome
Discomfort/pain

It is very uncertain whether vibrational appliance delivering light vibrational forces (Tooth… changes patient-centred outcome: pain and discomfort (VAS 0 to 100 mm), one… compared with conventional orthodontic treatment (with or without sham/placebo… (very low certainty).

What each review found

Current versions only
FindingEffectStudiesGRADE certainty
Vibrational appliance delivering light vibrational forces (Tooth Masseuse; OrthoAccel) vs Conventional orthodontic treatment (with or without sham/placebo device)
Patient-centred outcome: pain and discomfort (VAS 0 to 100 mm), one day after archwire · Orthodontic patients with malocclusion (any age) undergoing fixed appliance treatment · 1 day after ligating archwire
It is very uncertain whether vibrational appliance delivering light vibrational forces (Tooth… changes patient-centred outcome: pain and discomfort (VAS 0 to 100 mm), one… compared with conventional orthodontic treatment (with or without sham/placebo… (very low certainty).
Non‐surgical adjunctive interventions for accelerating tooth movement in patients undergoing orthodontic… (2015) · Cochrane All outcomesearlier version CD010887.PUB2, superseded
MD -6.1 [-18.80, 6.60]1 study · 64 participantsVery low

Findings are shown side by side, never pooled across reviews.

The reviews

ReviewSearched up toIncluded studiesAMSTAR 2
Non‐surgical adjunctive interventions for accelerating tooth movement in patients undergoing orthodontic treatment
2023 · Cochrane All outcomes
2022-09Not listedNot assessed

The same intervention and outcome, asked in another population or against another comparator.

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.