Question · asked by 1 review
Pre‐prandial vs post‐prandial glucose monitoring: Perinatal mortality
- Population
- women with pre‐existing diabetes
- Intervention
- Pre‐prandial
- Comparison
- post‐prandial glucose monitoring
- Outcome
- Perinatal mortality
It is very uncertain whether pre‐prandial changes perinatal mortality (stillbirth and neonatal mortality) compared with post‐prandial glucose monitoring (very low certainty).
What each review found
Current versions onlyNo studies found 0 · 0%GRADE not reported 0 · 0%Very low 2 · 100%Low 0 · 0%Moderate 0 · 0%High 0 · 0%
| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Pre‐prandial vs post‐prandial glucose monitoring Perinatal mortality (stillbirth and neonatal mortality) · women with pre‐existing diabetes It is very uncertain whether pre‐prandial changes perinatal mortality (stillbirth and neonatal mortality) compared with post‐prandial glucose monitoring (very low certainty). Techniques of monitoring blood glucose during pregnancy for women with pre‐existing diabetes (2019) · Cochrane Headline outcomes only | RR 2.91 [0.12, 68.66] | 1 study · 61 participants | Very low |
Pre‐prandial vs post‐prandial glucose monitoring Perinatal mortality · women with pre‐existing diabetes It is very uncertain whether pre‐prandial changes perinatal mortality compared with post‐prandial glucose monitoring (very low certainty). Techniques of monitoring blood glucose during pregnancy for women with pre‐existing diabetes (2017) · Cochrane Headline outcomes onlyearlier version CD009613.PUB3, superseded | RR 2.91 [0.12, 68.66] | 1 study · 61 participants | Very low |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Techniques of monitoring blood glucose during pregnancy for women with pre‐existing diabetes 2019 · Cochrane Headline outcomes only | Not reported | Not extracted | 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.