Occurrence and Predictors of Cesarean Delivery for the Second Twin After Vaginal Delivery of the First Twin
Bibliographic record
Abstract
OBJECTIVE: To estimate the occurrence and to assess clinical predictors of emergent cesarean delivery in the second twin after vaginal delivery of the first twin. METHODS: We conducted a population-based cohort study, using the 1995-1997 linked mother/infant twin data from the United States. The adjusted risk ratios and population attributable risks of clinical predictors of emergent cesarean delivery in second twins were estimated for the overall study sample and for those born at less than 36 or 36 weeks or more of gestation. RESULTS: Among the 61,845 second twin births with the first twin delivered vaginally, 5,842 (9.45%) were delivered by cesarean. The cesarean delivery rate was increased in infants born to mothers with medical or labor and delivery complications. Breech and other malpresentations were the most important predictors of emergent cesarean delivery for the second twin (population attributable risk 33.2%; 95% confidence interval 31.8%, 34.6%). Operative vaginal delivery of the first twin was associated with a decreased risk of cesarean delivery for the second twin. Prediction of emergent cesarean for the second twin by clinical factors was stronger in term births than preterm births. CONCLUSION: In the general population, the cesarean delivery rate for the second twin after vaginal delivery of the first twin is approximately 9.5%. With the presence of breech and other malpresentations, the need for emergent cesarean delivery of the second twin after vaginal delivery of the first twin is increased by 4-fold. LEVEL OF EVIDENCE: II-2
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".