Question · asked by 1 review
Professional oral care vs Usual oral care: Change in data on economics and quality of life
- Population
- Residents of nursing homes and other long-term care facilities
- Intervention
- Professional oral care
- Comparison
- Usual oral care
- Outcome
- Change in data on economics and quality of life
Professional oral care led to higher change in data on economics and quality of life compared with usual oral care; GRADE not reported.
What each review found
Current versions onlyNo studies found 0 · 0%GRADE not reported 2 · 100%Very low 0 · 0%Low 0 · 0%Moderate 0 · 0%High 0 · 0%
| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Professional oral care vs Usual oral care Change in data on economics and quality of life · Residents of nursing homes and other long-term care facilities · 24 months Professional oral care led to higher change in data on economics and quality of life compared with usual oral care; GRADE not reported. Oral care measures for preventing nursing home‐acquired pneumonia (2022) · Cochrane All outcomes | MD 1.5 [0.32, 2.68] | 1 study · 322 participants | GRADE not reported |
Professional oral care vs Usual oral care Change in data on economics and quality of life · Residents of nursing homes and other long-term care facilities · 24 months Oral care measures for preventing nursing home‐acquired pneumonia (2018) · Cochrane All outcomesearlier version CD012416.PUB2, superseded | Only one study measured quality of life. MMSE change at 24 months: intervention 1.5 + 4.9 (170 participants) vs control 3.0 + 5.9 (152 participants); P = 0.032. Professional oral care mitigated the age-related MMSE… | 1 study · 322 participants | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Oral care measures for preventing nursing home‐acquired pneumonia 2022 · Cochrane All outcomes | 12 May 2022 | 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.