Question · asked by 1 review
Primary (complete) wound closure vs Secondary (partial) wound closure: Postoperative pain (mean)
- Population
- Adults with mandibular third molars requiring removal
- Intervention
- Primary (complete) wound closure
- Comparison
- Secondary (partial) wound closure
- Outcome
- Postoperative pain (mean)
Primary (complete) wound closure led to higher postoperative pain (mean) compared with secondary (partial) wound closure; GRADE not reported.
What each review found
Current versions onlyNo studies found 0 · 0%GRADE not reported 1 · 50%Very low 0 · 0%Low 0 · 0%Moderate 1 · 50%High 0 · 0%
| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Primary (complete) wound closure vs Secondary (partial) wound closure Postoperative pain (mean) · Adults with mandibular third molars requiring removal · 24 hours Primary (complete) wound closure led to higher postoperative pain (mean) compared with secondary (partial) wound closure; GRADE not reported. Surgical techniques for the removal of mandibular wisdom teeth (2020) · Cochrane All outcomes | MD 0.94 [0.50, 1.38] | 5 studies · 474 participants | GRADE not reported |
Primary (complete) wound closure vs Secondary (partial) wound closure Postoperative pain (mean) · Adults with mandibular third molars requiring removal · 24 hours Primary (complete) wound closure probably leads to higher postoperative pain (mean) compared with secondary (partial) wound closure (moderate certainty). Surgical techniques for the removal of mandibular wisdom teeth (2014) · Cochrane All outcomesearlier version CD004345.PUB2, superseded | MD 0.79 [0.35, 1.24] | 4 studies · 414 participants | Moderate |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Surgical techniques for the removal of mandibular wisdom teeth 2020 · Cochrane All outcomes | 2019-07 | 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.