General obstetrics: A randomised controlled trial of metronidazole for the prevention of preterm birth in women positive for cervicovaginal fetal fibronectin: the PREMET Study
Bibliographic record
Abstract
OBJECTIVE: To determine whether metronidazole reduces early preterm labour in asymptomatic women with positive vaginal fetal fibronectin (fFN) in the second trimester of pregnancy. DESIGN: Randomised placebo-controlled trial. SETTING: Fourteen UK hospitals (three teaching). POPULATION: Pregnancies with at least one previous risk factor, including mid-trimester loss or preterm delivery, uterine abnormality, cervical surgery or cerclage. METHODS: Nine hundred pregnancies were screened for fFN at 24 and 27 weeks of gestation. Positive cases were randomised to a week's course of oral metronidazole or placebo. MAIN OUTCOME MEASURES: Primary outcome was delivery before 30 weeks of gestation. Secondary outcomes included delivery before 37 weeks. RESULTS: The Trial Steering Committee (TSC) recommended the study be stopped early; 21% of women receiving metronidazole (11/53) delivered before 30 weeks compared with 11% (5/46) taking placebo [risk ratio 1.9, 95% confidence interval (CI) 0.72-5.09, P = 0.18]. There were significantly more preterm deliveries (before 37 weeks) in women treated with metronidazole 33/53 (62%) versus placebo 18/46 (39%), risk ratio 1.6, 95% CI 1.05-2.4. fFN was a good predictor of early preterm birth in these asymptomatic women; positive and negative predictive values (24 weeks of gestation) for delivery by 30 weeks were 26% and 99%, respectively (positive and negative likelihood ratios 15, 0.35). CONCLUSION: Metronidazole does not reduce early preterm birth in high risk pregnant women selected by history and a positive vaginal fFN test. Preterm delivery may be increased by metronidazole therapy.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".