Question · asked by 1 review

Primary (complete) wound closure vs Secondary (partial) wound closure: Adverse effects (reactionary bleeding)

Population
Adults with mandibular third molars requiring removal
Intervention
Primary (complete) wound closure
Comparison
Secondary (partial) wound closure
Outcome
Adverse effects (reactionary bleeding)

It is very uncertain whether primary (complete) wound closure changes adverse effects (reactionary bleeding) compared with secondary (partial) wound closure (very low certainty).

What each review found

Current versions only
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
Primary (complete) wound closure vs Secondary (partial) wound closure
Adverse effects (reactionary bleeding) · Adults with mandibular third molars requiring removal · up to 30 days
It is very uncertain whether primary (complete) wound closure changes adverse effects (reactionary bleeding) compared with secondary (partial) wound closure (very low certainty).
Surgical techniques for the removal of mandibular wisdom teeth (2020) · Cochrane All outcomes
RR 0.41 [0.11, 1.47]1 study · 82 participantsVery low
Primary (complete) wound closure vs Secondary (partial) wound closure
Adverse effects (reactionary bleeding) · Adults with mandibular third molars requiring removal · up to 30 days
It is very uncertain whether primary (complete) wound closure changes adverse effects (reactionary bleeding) compared with secondary (partial) wound closure (very low certainty).
Surgical techniques for the removal of mandibular wisdom teeth (2014) · Cochrane All outcomesearlier version CD004345.PUB2, superseded
RR 0.41 [0.11, 1.47]1 study · 82 participantsVery low

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

The reviews

ReviewSearched up toIncluded studiesAMSTAR 2
Surgical techniques for the removal of mandibular wisdom teeth
2020 · Cochrane All outcomes
2019-07Not 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.