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 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 | 0.018 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.035 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".