Preterm birth prevention in twin pregnancies with progesterone, pessary, or cerclage: a systematic review and meta‐analysis
Bibliographic record
Abstract
Background About half of twin pregnancies deliver preterm, and it is unclear whether any intervention reduces this risk. Objectives To assess the evidence for the effectiveness of progesterone, cerclage, and pessary in twin pregnancies. Search strategy We searched Medline, EMBASE, CINAHL, Cochrane Central Register of Controlled Trials, and ISI Web of Science, without language restrictions, up to 25 January 2016. Selection criteria Randomised controlled trials of progesterone, cerclage, or pessary for preventing preterm birth in women with twin pregnancies, without symptoms of threatened preterm labour. Data collection and analysis Two independent reviewers extracted data using a piloted form. Study quality was appraised with the Cochrane Risk of Bias tool. We performed pairwise inverse variance random‐effects meta‐analyses. Main results We included 23 trials (all but three were considered to have a low risk of bias) comprising 6626 women with twin pregnancies. None of the interventions significantly reduced the risk of preterm birth overall at <34 or <37 weeks of gestation, or neonatal death, our primary outcomes, compared to a control group. In women receiving vaginal progesterone, the relative risk (RR) of preterm birth <34 weeks of gestation was 0.82 (95% CI 0.64–1.05, seven studies, I2 36%), with a significant reduction in some key secondary outcomes, including very low birthweight (<1500 g, RR 0.71, 95% CI 0.52–0.98, four studies, I2 46%) and mechanical ventilation (RR 0.61, 95% CI 0.45–0.82, four studies, I2 22%). Conclusion In twin gestations, although no overarching intervention was beneficial for the prevention of preterm birth and its sequelae, vaginal progesterone improved some important secondary outcomes. Tweetable abstract Vaginal progesterone may be beneficial in twin pregnancies, but not 17‐OHPC, cerclage, or pessary.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.030 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".