High feedback vs low feedback of routine prenatal ultrasound: Maternal anxiety
High feedback showed no clear difference in maternal anxiety ‐ throughout pregnancy compared with low feedback of routine prenatal ultrasound (low certainty).
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
High feedback vs low feedback of routine prenatal ultrasound Maternal anxiety ‐ throughout pregnancy · Pregnant women undergoing routine ultrasound during pregnancy High feedback showed no clear difference in maternal anxiety ‐ throughout pregnancy compared with low feedback of routine prenatal ultrasound (low certainty). High feedback versus low feedback of prenatal ultrasound for reducing maternal anxiety and improving… (2015) · Cochrane Headline outcomes only | SMD 0.1 | 3 studies · 346 participants | Low |
High feedback vs low feedback of routine prenatal ultrasound Maternal anxiety ‐ In the second trimester · Pregnant women undergoing routine ultrasound during pregnancy High feedback showed no clear difference in maternal anxiety ‐ In the second trimester compared with low feedback of routine prenatal ultrasound (low certainty). High feedback versus low feedback of prenatal ultrasound for reducing maternal anxiety and improving… (2015) · Cochrane Headline outcomes only | SMD 0.23 | 1 study · 40 participants | Low |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| High feedback versus low feedback of prenatal ultrasound for reducing maternal anxiety and improving maternal health behaviour in pregnancy 2015 · 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.