Objective: Shoulder dystocia (ShD) is a rare but serious obstetric emergency associated with significant neonatal morbidity. This study aimed to evaluate the predictive performance of machine learning (ML) models based on fetal biometric ratios and clinical characteristics for the identification of ShD in pregnancies without clinical suspicion of macrosomia. Methods: We conducted a retrospective case-control study including 284 women (84 ShD cases and 200 controls) who underwent spontaneous vaginal delivery between 37 and 42 weeks of gestation. All participants had an estimated fetal weight (EFW) below the 90th percentile according to Hadlock reference curves. Univariate and multivariate logistic regression analyses were performed on maternal and neonatal parameters, and statistically significant variables (p < 0.05) were used to construct adjusted odds ratio (aOR) models. Supervised ML models—Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGB)—were trained and tested to assess predictive accuracy. Performance metrics included AUC-ROC, sensitivity, specificity, accuracy, and F1-score. Results: The BPD/AC ratio and AC/FL ratio markedly enhanced the prediction of ShD. When added to other features in RF models, the BPD/AC ratio got an AUC of 0.884 (95% CI: 0.802–0.957), a sensitivity of 68%, and a specificity of 83%. On the other hand, the AC/FL ratio, along with other factors, led to an AUC of 0.896 (95% CI: 0.805–0.972), 68% sensitivity, and 90% specificity. Conclusions: In pregnancies without clinical suspicion of macrosomia, ML models integrating fetal biometric ratios with maternal and labor-related factors significantly improved the prediction of ShD. These models may support clinical decision-making in low-risk deliveries where ShD is often unexpected.

To evaluate maternal serum Elabela (ELA) levels in pregnancies complicated by placenta accreta spectrum (PAS) and to assess their association with disease

Infectious complications at the surgical site complicates 1–4% of cesarean deliveries. Strict aseptic technique, hand hygiene and the use of sterile surgical gloves are essential to reduce the risk of surgical site contamination. Glove perforations are observed in almost 30% of all procedures and represent an underestimated potential risk factor for postoperative infections. The aim of this prospective study was to investigate glove perforation rates in cesarean deliveries and to analyze the impact of unrecognized glove perforations during surgery on surgical site infection rates after cesarean delivery. Surgical gloves from cesarean section were collected prospectively. All gloves were inspected for perforation holes. Cases in which surgical gloves had visible (macro) perforations were excluded from the study. Remaining gloves were examined using a water-fill test. Gloves with a positive water filling test were defined as the study group and negative gloves as the control group. Superficial Surgical Site Infections (SSI) were analyzed for the groups. A total of 1,779 pairs of used surgical gloves were collected and examined for perforations across 593 cesarean operations. Macro perforation were observed in 56 (9.4%) operations. The remaining 1,611 pairs of gloves underwent water-fill testing. Of these, 505 operations had completely intact gloves, while in 32 (5.4%) cases, micro-perforations were identified. SSI occurred in 18 women (3.3%): 7 in the perforation group (21.8%) and 11 in the control group (2.2%) (p < 0.001). In multivariable logistic regression analysis, glove micro-perforation was found to be a significant independent predictor of SSI (adjusted odds ratio: 12.705; 95% CI: 4.244–38.039; p < 0.001), even after adjusting for potential confounders including diabetes, obesity, emergency cesarean, operative duration, and intraoperative blood loss. Our data show that glove perforation is unexpectedly common and underestimated in terms of unfavorable surgical outcomes. The rate of surgical site infections is increasing and we suggest that unrecognized glove perforation must be considered as a risk factor for surgical site infection after cesarean section.

To assess the structural and hemodynamic characteristics of the foramen ovale (FO) in fetuses with late-onset fetal growth restriction (LO-FGR) using three-dimensional (3D) ultrasonography and Doppler imaging, and to examine their associations with Doppler parameters in FGR and composite adverse perinatal outcomes (CAPO). This case-control study included 40 fetuses with LO-FGR and 40 matched controls exhibiting appropriate-for-gestational-age (AGA) between 34 and 37 weeks. FO area was measured using 3D spatio-temporal image correlation (STIC) imaging, and FO width and pulsatility index (PI) were evaluated using 2D and Doppler ultrasonography. FO parameters were compared between the groups, and partial correlation analyses adjusted for gestational age to assess their associations with FGR and CAPO. Additionally, Receiver Operating Characteristic (ROC) curve analysis was conducted to evaluate the predictive value of FO parameters for CAPO within the FGR group. FO area (p < 0.001), FO width (p < 0.001), left atrial (LA) width (p = 0.029), FO/LA ratio (p < 0.001), and FO/RA ratio (p = 0.024) were significantly reduced in the FGR group compared to the controls. Among FGR fetuses, those who developed CAPO had lower FO area (p = 0.009), FO width (p = 0.001), LA width (p = 0.006), FO/LA ratio (p < 0.001), and FO/RA ratio (p = 0.041). In ROC analysis, the FO/LA ratio exhibited the highest predictive value for predicting CAPO (AUC: 0.851, p < 0.001). Alterations in FO morphology are significantly associated with adverse perinatal outcomes in LO-FGR. The FO/LA ratio may serve as a reliable and noninvasive parameter for risk stratification. Incorporating advanced fetal cardiac morphometry could improve prenatal surveillance in FGR.

Comparative analysis of hormonal and metabolic indices in phenotypic subgroups of polycystic ovary syndrome - Journal of the Turkish-German Gynecological Association