Trial of labor versus elective repeat cesarean delivery in twin pregnancies after a previous cesarean delivery—A systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: To perform a systematic review of success rates of trial of labor after cesarean (TOLAC) and maternal and neonatal outcomes in twin pregnancy versus elective repeat cesarean delivery (ERCD). METHODS: We searched MEDLINE, EMBASE, and Web of Science from data inception to May 2018 with no language or regional restrictions, to identify all studies that compared twin TOLAC and ERCD for maternal and/or neonatal outcomes. The Newcastle-Ottawa Scale was used to assess the methodological quality of the included studies. We assessed the pooled relative risk and mean difference using a random-effects model. The pooled event rates for successful VBAC, cesarean delivery for twin B after vaginal delivery of twin A, and uterine rupture were determined. RESULTS: Of the 841 citations identified, 10 were eligible for analysis (2336 TOLAC cases and 5763 ERCD cases). The pooled event rates for successful VBAC and uterine rupture during TOLAC were 72.2% (95% CI 59.7%-83.2%) and 0.87% (95% CI 0.51%-1.31%), respectively. TOLAC was associated with a significantly higher risk of neonatal death (RR 3.02 [95% CI 1.07-8.54]) with no significant differences in mean gestational age at birth, NICU admission rates, or 5-minute Apgar <7. Although the risk for maternal infectious morbidity was significantly lower with TOLAC (RR 0.48 [95% CI 0.25-0.90]), risks of uterine dehiscence, blood transfusions, and hysterectomy were comparable. CONCLUSIONS: Twin TOLAC is associated with a relatively high rate of successful vaginal delivery and a low risk of uterine rupture. The finding of higher neonatal mortality rates may be influenced by prematurity, but requires further investigation.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.030 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".