Question · asked by 1 review
Lingual split with chisel vs Lingual split with bur: Temporary altered tongue sensation
- Population
- Adults with mandibular third molars requiring removal
- Intervention
- Lingual split with chisel
- Comparison
- Lingual split with bur
- Outcome
- Temporary altered tongue sensation
Lingual split with chisel showed no clear difference in temporary altered tongue sensation compared with lingual split with bur; GRADE not reported.
What each review found
Current versions onlyNo studies found 0 · 0%GRADE not reported 2 · 100%Very low 0 · 0%Low 0 · 0%Moderate 0 · 0%High 0 · 0%
| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Lingual split with chisel vs Lingual split with bur Temporary altered tongue sensation · Adults with mandibular third molars requiring removal · <1 month, chisel vs bur Lingual split with chisel showed no clear difference in temporary altered tongue sensation compared with lingual split with bur; GRADE not reported. Surgical techniques for the removal of mandibular wisdom teeth (2020) · Cochrane All outcomes | OR 3.06 [0.27, 34.48] | 1 study · 52 participants | GRADE not reported |
Lingual split with chisel vs Lingual split with bur Temporary altered tongue sensation · Adults with mandibular third molars requiring removal · <1 month, chisel vs bur Lingual split with chisel showed no clear difference in temporary altered tongue sensation compared with lingual split with bur; GRADE not reported. Surgical techniques for the removal of mandibular wisdom teeth (2014) · Cochrane All outcomesearlier version CD004345.PUB2, superseded | RR 3.00 [0.13, 71.99] | 1 study · 104 participants | GRADE not reported |
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.