Question · asked by 1 review

Abdominal dermal fat graft vs Nasolabial flap: Maximal mouth opening (interincisal distance)

Population
People of any age with a confirmed diagnosis, by clinical examination and biopsy, of oral submucous fibrosis (OSF)
Intervention
Abdominal dermal fat graft
Comparison
Nasolabial flap
Outcome
Maximal mouth opening (interincisal distance)

It is very uncertain whether abdominal dermal fat graft changes maximal mouth opening (interincisal distance) compared with nasolabial flap (very low certainty).

What each review found

Include earlier review versions
No studies found 0 · 0%GRADE not reported 0 · 0%Very low 2 · 100%Low 0 · 0%Moderate 0 · 0%High 0 · 0%
FindingEffectStudiesGRADE certainty
Abdominal dermal fat graft vs Nasolabial flap
Maximal mouth opening (interincisal distance) · People of any age with a confirmed diagnosis, by clinical examination and biopsy, of oral submucous fibrosis (OSF) · 3 to 6 months
It is very uncertain whether abdominal dermal fat graft changes maximal mouth opening (interincisal distance) compared with nasolabial flap (very low certainty).
Interventions for managing oral submucous fibrosis (2024) · Cochrane All outcomes
MD -14.87 [-19.93, -9.81]1 study · 30 participantsVery low
Abdominal dermal fat graft vs Nasolabial flap
Maximal mouth opening (interincisal distance) · People of any age with a confirmed diagnosis, by clinical examination and biopsy, of oral submucous fibrosis (OSF) · Less than 3 months
It is very uncertain whether abdominal dermal fat graft changes maximal mouth opening (interincisal distance) compared with nasolabial flap (very low certainty).
Interventions for managing oral submucous fibrosis (2024) · Cochrane All outcomes
MD -3.60 [-5.17, -2.03]1 study · 30 participantsVery low

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

The reviews

ReviewSearched up toIncluded studiesAMSTAR 2
Interventions for managing oral submucous fibrosis
2024 · Cochrane All outcomes
2022-09Not 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.