Vaginal progesterone, oral progesterone, 17‐OHPC, cerclage, and pessary for preventing preterm birth in at‐risk singleton pregnancies: an updated systematic review and network meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Recent progesterone trials call for an update of previous syntheses of interventions to prevent preterm birth. OBJECTIVES: To compare the relative effects of different types and routes of administration of progesterone, cerclage, and pessary at preventing preterm birth in at-risk women overall and in specific populations. SEARCH STRATEGY: We searched Medline, EMBASE, CINAHL, Cochrane CENTRAL, and Web of Science up to 1 January 2018. SELECTION CRITERIA: We included randomised trials of progesterone, cerclage or pessary for preventing preterm birth in at-risk singleton pregnancies. DATA COLLECTION AND ANALYSIS: We used a piloted data extraction form and performed Bayesian random-effects network meta-analyses with 95% credibility intervals (CrI), as well as pairwise meta-analyses, rating the quality of the evidence using GRADE. MAIN RESULTS: We included 40 trials (11 311 women). In at-risk women overall, vaginal progesterone reduced preterm birth <34 (OR 0.43, 95% CrI 0.20-0.81) and <37 weeks (OR 0.51, 95% CrI 0.34-0.74), and neonatal death (OR 0.41, 95% CrI 0.20-0.83). In women with a previous preterm birth, vaginal progesterone reduced preterm birth <34 (OR 0.29, 95% CI 0.12-0.68) and <37 weeks (OR 0.43, 95% CrI 0.23-0.74), and 17α-hydroxyprogesterone caproate reduced preterm birth <37 weeks (OR 0.53, 95% CrI 0.27-0.95) and neonatal death (OR 0.39, 95% CI 0.16-0.95). In women with a short cervix (≤25 mm), vaginal progesterone reduced preterm birth <34 weeks (OR 0.45, 95% CI 0.24-0.84). CONCLUSIONS: Vaginal progesterone was the only intervention with consistent effectiveness for preventing preterm birth in singleton at-risk pregnancies overall and in those with a previous preterm birth. TWEETABLE ABSTRACT: In updated NMA, vaginal progesterone consistently reduced PTB in overall at-risk pregnancies and in women with previous PTB.
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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 | 0.020 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.032 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| 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".