Question · asked by 1 review
Random forest classifier using KEGG Orthology features vs Caries versus healthy disease-state labels: External disease-state discrimination
- Population
- Oral microbiome samples from four selected case-control studies
- Intervention
- Random forest classifier using KEGG Orthology features
- Comparison
- Caries versus healthy disease-state labels
- Outcome
- External disease-state discrimination
The KEGG Orthology classifier had an approximate AUC of 0.75 in the disease-only study test set.
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Random forest classifier using KEGG Orthology features vs Caries versus healthy disease-state labels External disease-state discrimination · Oral microbiome samples from four selected case-control studies · Human oral samples The KEGG Orthology classifier had an approximate AUC of 0.75 in the disease-only study test set. Meta-analysis of caries microbiome studies can improve upon disease prediction outcomes. (2022) All outcomes | AUC 0.75 | 4 studies | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Meta-analysis of caries microbiome studies can improve upon disease prediction outcomes. 2022 All outcomes | Not specified (studies published from 2012 onward included; no explicit search date reported) | 16 | Not applicable |
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.