Effectiveness of progesterone, cerclage and pessary for preventing preterm birth in singleton pregnancies: a systematic review and network meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Preterm birth (PTB) is the leading cause of infant death, but it is unclear which intervention is best to prevent it. OBJECTIVES: To compare progesterone, cerclage and pessary, determine their relative effects and rank them. SEARCH STRATEGY: We searched Medline, EMBASE, CINAHL, Cochrane CENTRAL and Web of Science (to April 2016), without restrictions, and screened references of previous reviews. SELECTION CRITERIA: We included randomised trials of progesterone, cerclage or pessary for preventing PTB in women with singleton pregnancies at risk as defined by each study. DATA COLLECTION AND ANALYSIS: We extracted data by duplicate using a piloted form and performed Bayesian random-effects network meta-analyses and pairwise meta-analyses. We rated evidence quality using GRADE, ranked interventions using SUCRA and calculated numbers needed to treat (NNT). MAIN RESULTS: We included 36 trials (9425 women; 25 low risk of bias trials). Progesterone ranked first or second for most outcomes, reducing PTB < 34 weeks [odds ratio (OR) 0.44; 95% credible interval (CrI) 0.22-0.79; NNT 9; low quality], <37 weeks (OR 0.58; 95% CrI 0.41-0.79; NNT 9; moderate quality), and neonatal death (OR 0.50; 95% CrI 0.28-0.85; NNT 35; high quality), compared with control, in women overall at risk. We found similar results in the subgroup with previous PTB, but only a reduction of PTB < 34 weeks in women with a short cervix. Pessary showed inconsistent benefit and cerclage did not reduce PTB < 37 or <34 weeks. CONCLUSIONS: Progesterone was the best intervention for preventing PTB in singleton pregnancies at risk, reducing PTB < 34 weeks, <37 weeks, neonatal demise and other sequelae. TWEETABLE ABSTRACT: Progesterone was better than cerclage and pessary to prevent preterm birth, neonatal death and more in network meta-analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".