Artificial bone graft: InFuse (collagen matrix) impregnated with rhBMP-2 vs Traditional iliac crest cancellous bone graft: Clinical assessment: nasal alar base augmentation (0 to 3 grading scale)
- Population
- Skeletally mature patients with unilateral cleft lip and palate and an alveolar cleft defect (>5 years; mean age 16.1 years)
- Intervention
- Artificial bone graft: InFuse (collagen matrix) impregnated with rhBMP-2
- Comparison
- Traditional iliac crest cancellous bone graft
- Outcome
- Clinical assessment: nasal alar base augmentation (0 to 3 grading scale)
Artificial bone graft: InFuse (collagen matrix) impregnated with… showed no clear difference in clinical assessment: nasal alar base augmentation (0 to 3 grading… compared with traditional iliac crest cancellous bone graft; GRADE not reported.
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Artificial bone graft: InFuse (collagen matrix) impregnated with rhBMP-2 vs Traditional iliac crest cancellous bone graft Clinical assessment: nasal alar base augmentation (0 to 3 grading scale) · Skeletally mature patients with unilateral cleft lip and palate and an alveolar cleft defect (>5 years; mean age 16.1… Artificial bone graft: InFuse (collagen matrix) impregnated with… showed no clear difference in clinical assessment: nasal alar base augmentation (0 to 3 grading… compared with traditional iliac crest cancellous bone graft; GRADE not reported. Secondary bone grafting for alveolar cleft in children with cleft lip or cleft lip and palate (2011) · Cochrane All outcomes | MD 0.20 [-0.01, 0.41] | 1 study · 21 participants | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Secondary bone grafting for alveolar cleft in children with cleft lip or cleft lip and palate 2011 · Cochrane All outcomes | 2011-02 | 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.