Single‐component hydration intervention vs control: Incidence of delirium
It is very uncertain whether single‐component hydration intervention changes incidence of delirium NEECHAM Confusion Scale compared with control (very low certainty).
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Single‐component hydration intervention vs control Incidence of delirium NEECHAM Confusion Scale · people at risk of delirium in institutional long‐term care It is very uncertain whether single‐component hydration intervention changes incidence of delirium NEECHAM Confusion Scale compared with control (very low certainty). Interventions for preventing delirium in older people in institutional long‐term care (2019) · Cochrane Headline outcomes only | RR 0.85 [0.18, 4] | 1 study · 98 participants | Very low |
Single‐component hydration intervention vs control Incidence of delirium1 NEECHAM confusion scale · People at risk of delirium in institutional long term care It is very uncertain whether single‐component hydration intervention changes incidence of delirium1 NEECHAM confusion scale compared with control (very low certainty). Interventions for preventing delirium in older people in institutional long‐term care (2014) · Cochrane Headline outcomes onlyearlier version CD009537.PUB2, superseded | RR 0.85 [0.18, 4] | 1 study · 98 participants | Very low |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Interventions for preventing delirium in older people in institutional long‐term care 2019 · Cochrane Headline outcomes only | Not reported | list not extracted yet | Not assessed |
Studies behind the findings
None of these reviews ties its included studies to this outcome in the data here. Cochrane reviews and reviews with headline outcomes only do not list their studies yet.
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.