Referral letter based on the common-sense model (CSM) plus dental information guide (DIG) vs Referral letter based on the common-sense model (CSM) alone: Proportion of children with treated/untreated caries (receipt of dental care)
- Population
- School children, primary schools in the USA
- Intervention
- Referral letter based on the common-sense model (CSM) plus dental information guide (DIG)
- Comparison
- Referral letter based on the common-sense model (CSM) alone
- Outcome
- Proportion of children with treated/untreated caries (receipt of dental care)
It is very uncertain whether referral letter based on the common-sense model (CSM) plus dental… changes proportion of children with treated/untreated caries (receipt of… compared with referral letter based on the common-sense model (CSM) alone (very low certainty).
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Referral letter based on the common-sense model (CSM) plus dental information guide (DIG) vs Referral letter based on the common-sense model (CSM) alone Proportion of children with treated/untreated caries (receipt of dental care) · School children, primary schools in the USA · mean 8 months It is very uncertain whether referral letter based on the common-sense model (CSM) plus dental… changes proportion of children with treated/untreated caries (receipt of… compared with referral letter based on the common-sense model (CSM) alone (very low certainty). School dental screening programmes for oral health (2022) · Cochrane All outcomes | OR 1.30 [0.85, 1.99] | 1 study · 411 participants | Very low |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| School dental screening programmes for oral health 2022 · Cochrane All outcomes | 2021-10 | 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.