Learning disability (CLD) vs No learning disability (CNLD): Restorative index (RI), permanent teeth
The restorative index for permanent teeth could not be meta-analyzed (no variance data); it was lower or equal in children with learning disabilities in 4 of 9 studies, and the pattern suggested more extraction- than restoration-based care in CLD (certainty not graded).
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Learning disability (CLD) vs No learning disability (CNLD) Restorative index (RI), permanent teeth · Children aged 1-18 years · Community and clinical settings (specialist paediatric dental clinics to school classrooms); 25 studies across 18 countries The restorative index for permanent teeth could not be meta-analyzed (no variance data); it was lower or equal in children with learning disabilities in 4 of 9 studies, and the pattern suggested more extraction- than restoration-based care in CLD (certainty not graded). Dental caries experience, care index and restorative index in children with learning disabilities and… (2019) All outcomes | Mean restorative index CLD 0.15 (SD 0.14) vs CNLD 0.28 (SD 0.33) for permanent teeth; RI in CLD was less than or equal to CNLD in 4 of 9 studies. Not meta-analyzed (no variance data). In CLD the RI tended to exceed the… | 9 studies | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Dental caries experience, care index and restorative index in children with learning disabilities and children without learning… 2019 All outcomes | Database inception to June 2017; search updated December 2018 | 44 | Not applicable |
Studies behind the findings
None of these reviews ties its included studies to this outcome in the data here. Cochrane reviews and reviews with headline outcomes only do not list their studies yet.
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.