Question · asked by 1 review

Study-specific random forest classifier vs Caries versus healthy disease-state labels: Study-specific disease-state discrimination

Population
Oral microbiome samples in 15 case-controlled studies
Intervention
Study-specific random forest classifier
Comparison
Caries versus healthy disease-state labels
Outcome
Study-specific disease-state discrimination

Bong-Soo 2017, Zhu 2018, and Jiang 2019 had mean AUC scores over 0.75; exact study-specific AUCs were not reported in text or readable figure labels.

What each review found

Current versions only
FindingEffectStudiesGRADE certainty
Study-specific random forest classifier vs Caries versus healthy disease-state labels
Study-specific disease-state discrimination · Oral microbiome samples in 15 case-controlled studies · Human oral samples
Bong-Soo 2017, Zhu 2018, and Jiang 2019 had mean AUC scores over 0.75; exact study-specific AUCs were not reported in text or readable figure labels.
Meta-analysis of caries microbiome studies can improve upon disease prediction outcomes. (2022) All outcomes
no estimate extracted15 studiesGRADE not reported

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

The reviews

ReviewSearched up toIncluded studiesAMSTAR 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)16Not 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.