Outcomes of non-vertex second twins, following vertex vaginal delivery of first twin: a secondary analysis of the WHO Global Survey on Maternal and Perinatal Health
Bibliographic record
Abstract
BACKGROUND: Mode of delivery remains a topic of debate in vertex/non-vertex twin pregnancies. We used the WHO Global Survey dataset to determine the risk of adverse maternal/perinatal outcomes associated with presentation of the second twin, following vaginal delivery of a vertex first twin. METHODS: We analysed a derived dataset of twin pregnancies ≥ 32 weeks gestation where the first twin was vertex and delivered vaginally. Maternal, delivery and neonatal characteristics and adverse outcomes were reported by presentation of the second twin. Logistic regression models (adjusted for maternal and perinatal confounders, mode of delivery and region) were developed to determine odds of adverse outcomes associated with presentation. RESULTS: 1,424 twin pregnancies were included, 25.9% of these had a non-vertex second twin and Caesarean was more common in non-vertex presentations (6.2% vs 0.9%, p < 0.001). While the odds of Apgar < 7 at 5 minutes were higher in non-vertex presenting second twins (16.0% vs 11.4%, AOR 1.42 95% CI 1.01-2.00), the odds of maternal ICU admission (4.6% vs 1.7%, AOR 1.30, 95% CI 0.88-1.94), blood transfusion (6.0% vs 3.4%, AOR 1.23, 95% CI 0.67-2.25), stillbirth (7.6% vs 4.7%, AOR 1.15, 95% CI 0.72-1.73), early neonatal death (3.8% vs 2.1%, AOR 1.68, 95% CI 0.96-2.94), and NICU admission (26.6% vs 23.2%, AOR 0.93, 95% CI 0.62-1.39) were not. CONCLUSION: After a vaginal delivery of a vertex first twin, non-vertex presentation of the second twin is associated with increased odds of Apgar <7 at 5 minutes, but not of other maternal/perinatal outcomes. Presentation of the second twin is not as important a consideration in planning twin vaginal birth as previously considered.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".