All exposure-OHRQoL estimates pooled for publication-bias correction (meta-trim sensitivity); specific exposure set not defined in source vs Not specified: Poor oral health-related quality of life (OHRQoL)
A trim-and-fill sensitivity analysis pooling every exposure estimate gave an overall OR of 0.17 (95% CI 0.13-0.21) after correcting for significant publication bias (Fig. 13); this combined estimate spans heterogeneous exposures and is a publication-bias sensitivity result (ungraded).
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
All exposure-OHRQoL estimates pooled for publication-bias correction (meta-trim sensitivity); specific exposure set not defined in source vs Not specified Poor oral health-related quality of life (OHRQoL) · Children aged 3-12 years · Children across multiple countries (Brazil, Hong Kong, Sri Lanka, New Zealand), Trim-and-fill adjusted (publication-bias corrected), random-effects A trim-and-fill sensitivity analysis pooling every exposure estimate gave an overall OR of 0.17 (95% CI 0.13-0.21) after correcting for significant publication bias (Fig. 13); this combined estimate spans heterogeneous exposures and is a publication-bias sensitivity result (ungraded). The Association of Oral Health Status, demographic characteristics and socioeconomic determinants with Oral… (2020) All outcomes | OR 0.17 [0.13, 0.21] | Not reported | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| The Association of Oral Health Status, demographic characteristics and socioeconomic determinants with Oral health-related quality of life… 2020 All outcomes | Up to December 2019 | 21 | 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.