4% articaine plain vs 4% articaine, 1:100,000 epinephrine: Duration of anaesthesia (pulpal, electric pulp tester; healthy pulps)
- Population
- Participants of any age and gender undergoing dental procedures, and volunteers who took part in simulated scenario studies in which dental local anaesthesia was tested
- Intervention
- 4% articaine plain
- Comparison
- 4% articaine, 1:100,000 epinephrine
- Outcome
- Duration of anaesthesia (pulpal, electric pulp tester; healthy pulps)
It is very uncertain whether 4% articaine plain changes duration of anaesthesia (pulpal, electric pulp tester; healthy pulps) compared with 4% articaine, 1:100,000 epinephrine (very low certainty).
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
4% articaine plain vs 4% articaine, 1:100,000 epinephrine Duration of anaesthesia (pulpal, electric pulp tester; healthy pulps) · Participants of any age and gender undergoing dental procedures, and volunteers who took part in simulated scenario… It is very uncertain whether 4% articaine plain changes duration of anaesthesia (pulpal, electric pulp tester; healthy pulps) compared with 4% articaine, 1:100,000 epinephrine (very low certainty). Injectable local anaesthetic agents for dental anaesthesia (2018) · Cochrane All outcomes | MD -37.08 [-60.95, -13.21] | 2 studies · 100 participants | Very low |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Injectable local anaesthetic agents for dental anaesthesia 2018 · Cochrane All outcomes | 2018-01 | Not listed | Not assessed |
Related questions
The same intervention and outcome, asked in another population or against another comparator.
- 4% articaine plain vs 4% articaine, 1:200,000 epinephrine · Participants of any age and gender undergoing dental procedures, and volunteers who took… · 1 review
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.