Question · asked by 1 review

Occlusal splint vs no treatment: Increase in the size of wear facets (canine teeth)

Population
People with sleep bruxism (tooth grinding or clenching); children (greater than 1 year old) and adults; diagnosis clinical, polysomnographic, or both
Intervention
Occlusal splint
Comparison
no treatment
Outcome
Increase in the size of wear facets (canine teeth)

Occlusal splint showed no clear difference in increase in the size of wear facets (canine teeth) compared with no treatment; GRADE not reported.

What each review found

Current versions only
No studies found 0 · 0%GRADE not reported 2 · 100%Very low 0 · 0%Low 0 · 0%Moderate 0 · 0%High 0 · 0%
FindingEffectStudiesGRADE certainty
Occlusal splint vs No treatment
Increase in the size of wear facets (canine teeth) · People with sleep bruxism (tooth grinding or clenching); children (greater than 1 year old) and adults; diagnosis… · children (age 3 to 5 years)
Occlusal splint showed no clear difference in increase in the size of wear facets (canine teeth) compared with no treatment; GRADE not reported.
Occlusal splints for treating sleep bruxism (tooth grinding) (2007) · Cochrane All outcomes
RR 0.20 [0.03, 1.15]1 studyGRADE not reported
Occlusal splint vs No treatment
Increase in the size of wear facets (canine teeth) · People with sleep bruxism (tooth grinding or clenching); children (greater than 1 year old) and adults; diagnosis… · 6 months of follow up, children (age 3 to 5 years)
Occlusal splint showed no clear difference in increase in the size of wear facets (canine teeth) compared with no treatment; GRADE not reported.
Occlusal splints for treating sleep bruxism (tooth grinding) (2007) · Cochrane All outcomes
RR 0.20 [0.03, 1.15]1 studyGRADE not reported

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

The reviews

ReviewSearched up toIncluded studiesAMSTAR 2
Occlusal splints for treating sleep bruxism (tooth grinding)
2007 · Cochrane All outcomes
2007-05Not listedNot 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.