ArticleBMJ open2026
Optimal timing of induction of labour to improve maternal and perinatal outcomes: protocol for an individual participant data and network meta-analysis.
Article in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionDespite advances in maternity care, stillbirth remains a major burden. It disproportionately affects black and Asian mothers, those with obesity and women over the age of 35 years. Induction of labour may benefit these women, but there is no clear evidence to guide recommendations on optimal timing of induction because of variations in the intervention and insufficient power in primary trials for rare outcomes such as stillbirth and perinatal mortality, or to assess whether effects differ by maternal characteristics. We will conduct an individual participant data (IPD) meta-analysis of randomised trials to assess the overall and differential effect of induction of labour, according to timing of induction and maternal characteristics, on adverse perinatal and maternal outcomes. We will also rank induction of labour timing strategies by their effectiveness to inform clinical and policy decision-making. METHODS AND ANALYSIS: We will identify randomised trials on induction of labour by searching MEDLINE, CINAHL, EMBASE, BIOSIS, LILACS, Pascal, SCI, CDSR, ClinicalTrials.gov, ICTRP, ISRCTN registry, CENTRAL, DARE and Health Technology Assessment Database, without language restrictions, from inception to June 2025. Primary researchers of identified trials will be invited to join the OPTIMAL Collaboration and share the original trial data. Data integrity and trustworthiness assessment will be performed on all eligible trials. We will check each study's IPD for consistency with the original authors before standardising and harmonising the data. Study quality of included trials will be assessed by the Cochrane Risk of Bias tool. We will perform a series of one-and-two-stage random-effects meta-analyses to obtain the summary intervention effect on composite adverse perinatal outcome (stillbirth, neonatal death or severe morbidity requiring admission to neonatal unit) with 95% CIs and summary treatment-covariate interactions (maternal age, ethnicity, parity, socioeconomic status, body mass index and method of conception). Heterogeneity will be summarised using tau ETHICS AND DISSEMINATION: The study is registered on PROSPERO (CRD420251066346) and ethics approval is not required. We will disseminate findings widely to women, healthcare professionals and policymakers through academic, professional bodies and social media channels, and in peer-reviewed journals to achieve impact. PROSPERO REGISTRATION NUMBER: CRD420251066346.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.