color contrast: black vs dark brown/light brown/yellow vs hardness contrast: soft vs medium/hard: total microbial load (log10 CFU)
In one study, hardness category differences exceeded color category differences; no meta-analysis was conducted.
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
color contrast: black vs dark brown/light brown/yellow vs hardness contrast: soft vs medium/hard total microbial load (log10 CFU) · humans with open primary dentine caries · in vivo dental clinical examinations, B-DB/LB/Y vs S-M/H In one study, hardness category differences exceeded color category differences; no meta-analysis was conducted. Reliability of colour and hardness clinical examinations in detecting dentine caries severity: a systematic… (2019) All outcomes | no estimate extracted | 1 study · 59 participants | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Reliability of colour and hardness clinical examinations in detecting dentine caries severity: a systematic review and meta-analysis. 2019 All outcomes | 1 April 2018 | 8 | Not 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.
- Partial caries removal in primary teeth: association of clinical parameters with microbiological status. · 2011 · included by 1 of these review
- A clinical, microbiologic, and radiographic study of deep caries lesions after incomplete caries removal. · 2002 · included by 1 of these review
- A clinical and microbiological study of deep carious lesions during stepwise excavation using long treatment intervals. · 1997 · included by 1 of these review
- A microbiological study of primary root-caries lesions with different treatment needs. · 1993 · included by 1 of these review
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.