MEDICS Dataset

This dataset contains granular patient-level data from the MEDICS randomized controlled trial comparing Foley catheter balloon mechanical induction and prostaglandin E2 induction in women undergoing trial of labor after one previous caesarean section. Core maternal demographics—age, BMI, ethnicity, parity, prior caesarean indications—and pre-induction cervical status are captured with precision, along with a systematic stratification framework based on history of previous normal vaginal delivery (NVD). Each participant is assigned a strata label: Pxx for those with a previous NVD and Nxx for those without. This stratification is clinically meaningful given the strong predictive value of prior vaginal birth for VBAC success and labour dynamics.The dataset includes serial Modified Bishop Score assessments, induction-to-labour and induction-to-delivery intervals, oxytocin augmentation, analgesia use, and all intrapartum complications (fever, abnormal CTG, hyperstimulation, meconium, balloon issues). Delivery outcomes capture mode of birth, indications for emergency caesarean, and objective markers relevant to uterine scar performance. Maternal postpartum outcomes range from estimated blood loss and postpartum haemorrhage to readmission and wound complications. Neonatal outcomes include birthweight, APGAR scores, cord pH thresholds, NICU admission, and adverse neonatal events.Patient-reported outcome measures—satisfaction, acceptability, and willingness to repeat or recommend the induction method—are also included, with >90% response rates. The structure of the data allows the replication of the trial’s core findings: higher cervical ripening success and fewer intrapartum complications with mechanical induction, similar VBAC and labour durations across arms, and no observed scar ruptures.Overall, this is a high-fidelity, stratified clinical dataset enabling nuanced comparative analyses of induction efficacy, safety, labour trajectories, and patient experience in TOLAC populations. The Pxx/Nxx stratification enhances modelling accuracy for VBAC prediction, induction success, and risk-adjusted outcome benchmarking within the MEDICS analytical ecosystem.