Vaginal progesterone is associated with a decrease in risk for early preterm birth and improved neonatal outcome in women with a short cervix: a secondary analysis from a randomized, double‐blind, placebo‐controlled trial
Bibliographic record
Abstract
OBJECTIVE: To investigate the efficacy of vaginal progesterone to prevent early preterm birth in women with sonographic evidence of a short cervical length in the midtrimester. METHODS: This was a planned, but modified, secondary analysis of our multinational, multicenter, randomized, placebo-controlled trial, in which women were randomized between 18 + 0 and 22 + 6 weeks of gestation to receive daily treatment with 90 mg of vaginal progesterone gel or placebo. Cervical length was measured with transvaginal ultrasound at enrollment and at 28 weeks of gestation. Treatment continued until either delivery, 37 weeks of gestation or development of preterm rupture of membranes. Maternal and neonatal outcomes were evaluated for the subset of all randomized women with cervical length < 28 mm at enrollment. The primary outcome was preterm birth at </= 32 weeks. RESULTS: A cervical length < 28 mm was identified in 46 randomized women: 19 of 313 who received progesterone and 27 of 307 who received the placebo. Baseline characteristics of the two groups were similar. In women with a cervical length < 28 mm, the rate of preterm birth at </= 32 weeks was significantly lower for those receiving progesterone than it was for those receiving the placebo (0% vs. 29.6%, P = 0.014). With progesterone, there were fewer admissions into the neonatal intensive care unit (NICU; 15.8% vs. 51.9%, P = 0.016) and shorter NICU stays (1.1 vs. 16.5 days, P = 0.013). There was also a trend toward a decreased rate of neonatal respiratory distress syndrome (5.3% vs. 29.6%, P = 0.060). CONCLUSION: Vaginal progesterone may reduce the rate of early preterm birth and improve neonatal outcome in women with a short sonographic cervical length.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".