Intensity of glycaemic control for women with gestational diabetes mellitus ‐ strict glycaemic targets vs less‐tight glycaemic targets (maternal outcomes): Caesarean section
- Population
- Women with GDM
- Intervention
- Intensity of glycaemic control for women with gestational diabetes mellitus ‐ strict glycaemic targets
- Comparison
- less‐tight glycaemic targets (maternal outcomes)
- Outcome
- Caesarean section
It is very uncertain whether intensity of glycaemic control for women with gestational diabetes… changes caesarean section compared with liberal glycaemic targets (Maternal outcomes) (very low certainty).
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Intensity of glycaemic control for women with gestational diabetes mellitus ‐ strict glycaemic targets vs liberal glycaemic targets (Maternal outcomes) Caesarean section · Women with GDM It is very uncertain whether intensity of glycaemic control for women with gestational diabetes… changes caesarean section compared with liberal glycaemic targets (Maternal outcomes) (very low certainty). Different intensities of glycaemic control for women with gestational diabetes mellitus (2016) · Cochrane Headline outcomes onlyearlier version CD011624.PUB2, superseded | RR 1.35 [0.83, 2.18] | 1 study · 171 participants | Very low |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Different intensities of glycaemic control for women with gestational diabetes mellitus 2023 · Cochrane Headline outcomes only | Not reported | Not extracted | Not assessed |
Related questions
The same intervention and outcome, asked in another population or against another comparator.
- Intensity of glycaemic control for women with gestational diabetes mellitus ‐ strict glycaemic targets vs less‐tight glycaemic targets (maternal outcomes) · women with gestational diabetes mellitus · 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.