Question · asked by 1 review

10% HP strip vs placebo: Tooth whitening - assessed by the dentist

Population
Adults (>=18 years) undergoing home-based bleaching (including tetracycline- and smoking-stained teeth, any baseline shade)
Intervention
10% HP strip
Comparison
placebo
Outcome
Tooth whitening - assessed by the dentist

It is very uncertain whether 10% HP strip changes tooth whitening - assessed by the dentist compared with placebo (very low certainty).

What each review found

Include earlier review versions
No studies found 0 · 0%GRADE not reported 0 · 0%Very low 2 · 100%Low 0 · 0%Moderate 0 · 0%High 0 · 0%
FindingEffectStudiesGRADE certainty
10% HP strip vs Placebo
Tooth whitening - assessed by the dentist · Adults (>=18 years) undergoing home-based bleaching (including tetracycline- and smoking-stained teeth, any baseline… · 15 days
It is very uncertain whether 10% HP strip changes tooth whitening - assessed by the dentist compared with placebo (very low certainty).
Home‐based chemically‐induced whitening (bleaching) of teeth in adults (2018) · Cochrane All outcomes
MD 1.93 [1.34, 2.52]1 study · 40 participantsVery low
10% HP strip vs Placebo
Tooth whitening - assessed by the dentist · Adults (>=18 years) undergoing home-based bleaching (including tetracycline- and smoking-stained teeth, any baseline… · day 8
It is very uncertain whether 10% HP strip changes tooth whitening - assessed by the dentist compared with placebo (very low certainty).
Home‐based chemically‐induced whitening (bleaching) of teeth in adults (2018) · Cochrane All outcomes
MD 2.24 [1.72, 2.76]1 study · 36 participantsVery low

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

The reviews

ReviewSearched up toIncluded studiesAMSTAR 2
Home‐based chemically‐induced whitening (bleaching) of teeth in adults
2018 · Cochrane All outcomes
2018-06Not listedNot assessed

The same intervention and outcome, asked in another population or against another comparator.

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.