Question · asked by 1 review

Gene sequencing (GS) applied to diagnose/manage oral conditions (predominantly 16S rRNA next-generation sequencing of the oral microbiome; also genome-wide/whole-genome sequencing) vs Negligible ('abysmal') effect of gene sequencing; no separate comparator arm (within-sample event contrast in the forest plots): Reduction in occurrence of oral conditions

Pooled across 8 clinical trials, gene sequencing was associated with a statistically significant risk difference for impact on oral-cavity disorders (RD -0.29, 95% CI -0.32 to -0.26); certainty not GRADE-rated.

What each review found

Current versions only
FindingEffectStudiesGRADE certainty
Gene sequencing (GS) applied to diagnose/manage oral conditions (predominantly 16S rRNA next-generation sequencing of the oral microbiome; also genome-wide/whole-genome sequencing) vs Negligible ('abysmal') effect of gene sequencing; no separate comparator arm (within-sample event contrast in the forest plots)
Reduction in occurrence of oral conditions · Human patients (any age) with oral-cavity related disorders (e.g. dental caries, periodontitis, gingivitis,… · Various countries; clinical trials of the oral microbiome/genetics, Overall
Pooled across 8 clinical trials, gene sequencing was associated with a statistically significant risk difference for impact on oral-cavity disorders (RD -0.29, 95% CI -0.32 to -0.26); certainty not GRADE-rated.
Gene sequencing applications to combat oral-cavity related disorders: a systematic review with meta-analysis. (2024) All outcomes
RD -0.29 [-0.32, -0.26]8 studies · 2211 participantsGRADE not reported

Findings are shown side by side, never pooled across reviews.

The reviews

ReviewSearched up toIncluded studiesAMSTAR 2
Gene sequencing applications to combat oral-cavity related disorders: a systematic review with meta-analysis.
2024 All outcomes
Not reported (search restricted to studies published 2015 to 2022; exact search-run date not given)8Not applicable

Studies behind the findings

The primary studies the full-text reviews tie to this outcome. Reviews with headline outcomes only do not list them here.

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.