Antimicrobial coolant A (chlorhexidine) vs Antimicrobial coolant B (cinnamon extract): Reduction in level of contamination in aerosols (CFU): chlorhexidine vs cinnamon extract coolant during ultrasonic scaling
- Population
- People undergoing aerosol-generating procedures (AGPs) in a dental setting
- Intervention
- Antimicrobial coolant A (chlorhexidine)
- Comparison
- Antimicrobial coolant B (cinnamon extract)
- Outcome
- Reduction in level of contamination in aerosols (CFU): chlorhexidine vs cinnamon extract coolant during ultrasonic scaling
It is very uncertain whether antimicrobial coolant A (chlorhexidine) changes reduction in level of contamination in aerosols (CFU): chlorhexidine… compared with antimicrobial coolant B (cinnamon extract) (very low certainty).
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Antimicrobial coolant A (chlorhexidine) vs Antimicrobial coolant B (cinnamon extract) Reduction in level of contamination in aerosols (CFU): chlorhexidine vs cinnamon extract coolant during ultrasonic scaling · People undergoing aerosol-generating procedures (AGPs) in a dental setting It is very uncertain whether antimicrobial coolant A (chlorhexidine) changes reduction in level of contamination in aerosols (CFU): chlorhexidine… compared with antimicrobial coolant B (cinnamon extract) (very low certainty). Interventions to reduce contaminated aerosols produced during dental procedures for preventing infectious… (2020) · Cochrane All outcomes | MD -11.90 [-35.88, 12.08] | 1 study · 40 participants | Very low |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Interventions to reduce contaminated aerosols produced during dental procedures for preventing infectious diseases 2020 · Cochrane All outcomes | 2020-09 | Not listed | Not assessed |
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.