Question · asked by 1 review
Delicate surgery and wound closure by primary intention vs Non-traumatic tooth avulsion and wound closure by secondary intention: MRONJ (incidence proportion)
- Population
- People treated with oral bisphosphonates undergoing dental extractions (at risk of MRONJ)
- Intervention
- Delicate surgery and wound closure by primary intention
- Comparison
- Non-traumatic tooth avulsion and wound closure by secondary intention
- Outcome
- MRONJ (incidence proportion)
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 |
|---|---|---|---|
Delicate surgery and wound closure by primary intention vs Non-traumatic tooth avulsion and wound closure by secondary intention Incidence of MRONJ · People treated with oral bisphosphonates undergoing dental extractions (at risk of MRONJ) Interventions for managing medication‐related osteonecrosis of the jaw (2022) · Cochrane All outcomes | In both study arms, no case of postoperative MRONJ was observed (700 participants; 1 RCT); effect not estimable. | 1 study · 700 participants | GRADE not reported |
Delicate surgery and wound closure by primary intention vs Non-traumatic tooth avulsion and wound closure by secondary intention Incidence of MRONJ · People treated with oral bisphosphonates undergoing dental extractions (at risk of MRONJ) Interventions for managing medication‐related osteonecrosis of the jaw (2017) · Cochrane All outcomesearlier version CD012432.PUB2, superseded | In both study arms, no case of postoperative MRONJ was observed (700 participants; 1 RCT); effect not estimable. | 1 study · 700 participants | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Interventions for managing medication‐related osteonecrosis of the jaw 2022 · Cochrane All outcomes | 2021-06 | 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.