Question · asked by 1 review

Colour clinical examination (visual shade categorization: yellow, light brown, dark brown, black) vs Hardness clinical examination (tactile texture categorization: soft, medium/leathery, hard): Discriminant power for caries severity: effect size (f) for microbial load (log10 CFU) between lesion categories

Population
Patients with dentine carious lesions (deciduous/permanent teeth; children and adults)
Intervention
Colour clinical examination (visual shade categorization: yellow, light brown, dark brown, black)
Comparison
Hardness clinical examination (tactile texture categorization: soft, medium/leathery, hard)
Outcome
Discriminant power for caries severity: effect size (f) for microbial load (log10 CFU) between lesion categories

The weighted effect size favoured hardness (f=2.62) over colour (f=0.46) for discriminating microbial load, supporting hardness as the more reliable examination (ungraded).

What each review found

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FindingEffectStudiesGRADE certainty
Colour clinical examination (visual shade categorization: yellow, light brown, dark brown, black) vs Hardness clinical examination (tactile texture categorization: soft, medium/leathery, hard)
Discriminant power for caries severity: effect size (f) for microbial load (log10 CFU) between lesion categories · Patients with dentine carious lesions (deciduous/permanent teeth; children and adults)
The weighted effect size favoured hardness (f=2.62) over colour (f=0.46) for discriminating microbial load, supporting hardness as the more reliable examination (ungraded).
Reliability of colour and hardness clinical examinations in detecting dentine caries severity: a systematic… (2019) All outcomes
Effect size (f) colour f=0.46; hardness f=2.625 studiesGRADE not reported

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

The reviews

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.