Povidone iodine vs Ozonated water: Reduction in level of contamination by particles >5 um (droplets) at <2 m from patient's oral cavity
- Population
- Dental patients undergoing aerosol-generating procedures (AGPs) and the dental healthcare providers treating them
- Intervention
- Povidone iodine
- Comparison
- Ozonated water
- Outcome
- Reduction in level of contamination by particles >5 um (droplets) at <2 m from patient's oral cavity
It is very uncertain whether povidone iodine changes reduction in level of contamination by particles >5 um (droplets) at… compared with ozonated water (very low certainty).
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Povidone iodine vs Ozonated water Reduction in level of contamination by particles >5 um (droplets) at <2 m from patient's oral cavity · Dental patients undergoing aerosol-generating procedures (AGPs) and the dental healthcare providers treating them · operator level; aerobic cultures It is very uncertain whether povidone iodine changes reduction in level of contamination by particles >5 um (droplets) at… compared with ozonated water (very low certainty). Preprocedural mouth rinses for preventing transmission of infectious diseases through aerosols in dental… (2022) · Cochrane All outcomes | MD 3.5 [-17.1, 24.1] | 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 |
|---|---|---|---|
| Preprocedural mouth rinses for preventing transmission of infectious diseases through aerosols in dental healthcare providers 2022 · Cochrane All outcomes | 2022-02 | Not listed | Not assessed |
Related questions
The same intervention and outcome, asked in another population or against another comparator.
- Povidone iodine vs Normal saline · Dental patients undergoing aerosol-generating procedures (AGPs) and the dental… · 1 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.