Management of fetal malposition in the second stage of labor: a propensity score analysis.

OBJECTIVE: We sought to determine the factors associated with selection of rotational instrumental vs cesarean delivery to manage persistent fetal malposition, and to assess differences in adverse neonatal and maternal outcomes following delivery by rotational instruments vs cesarean delivery. STUDY DESIGN: We conducted a retrospective cohort study over a 5-year period in a tertiary United Kingdom obstetrics center. In all, 868 women with vertex-presenting, single, liveborn infants at term with persistent malposition in the second stage of labor were included. Propensity score stratification was used to control for selection bias: the possibility that obstetricians may systematically select more difficult cases for cesarean delivery. Linear and logistic regression models were used to compare maternal and neonatal outcomes for delivery by rotational forceps or ventouse vs cesarean delivery, adjusting for propensity scores. RESULTS: Increased likelihood of rotational instrumental delivery was associated with lower maternal age (odds ratio [OR], 0.95; P < .01), lower body mass index (OR, 0.94; P < .001), lower birthweight (OR, 0.95; P < .01), no evidence of fetal compromise at the time of delivery (OR, 0.31; P < .001), delivery during the daytime (OR, 1.45; P < .05), and delivery by a more experienced obstetrician (OR, 7.21; P < .001). Following propensity score stratification, there was no difference by delivery method in the rates of delayed neonatal respiration, reported critical incidents, or low fetal arterial pH. Maternal blood loss was higher in the cesarean group (295.8 ± 48 mL, P < .001). CONCLUSION: Rotational instrumental delivery is often regarded as unsafe. However, we find that neonatal outcomes are no worse once selection bias is accounted for, and that the likelihood of severe obstetric hemorrhage is reduced. More widespread training of obstetricians in rotational instrumental delivery should be considered, particularly in light of rising cesarean delivery rates.During data analysis, A.R.A. was supported by an NICHD Predoctoral Fellowship under grant number F31HD079182 and by grant R24HD042849, awarded to the Population Research Center at The University of Texas at Austin. She is currently supported by grant R24HD047879 for Population Research at Princeton University. J.G.S. is partially funded by a CAREER grant from the National Science Foundation (DMS-1255187).This is the accepted version. It will be embargoed until 12 months after the final version is published by Elsevier. The final version is available from Elsevier at http://www.sciencedirect.com/science/article/pii/S000293781401078

Advancing Artificial Intelligence for Clinical Knowledge Retrieval: A Case Study Using ChatGPT-4 and Link Retrieval Plug-In to Analyze Diabetic Ketoacidosis Guidelines.

Introduction This case study aimed to enhance the traceability and retrieval accuracy of ChatGPT-4 in medical text by employing a step-by-step systematic approach. The focus was on retrieving clinical answers from three international guidelines on diabetic ketoacidosis (DKA). Methods A systematic methodology was developed to guide the retrieval process. One question was asked per guideline to ensure accuracy and maintain referencing. ChatGPT-4 was utilized to retrieve answers, and the 'Link Reader' plug-in was integrated to facilitate direct access to webpages containing the guidelines. Subsequently, ChatGPT-4 was employed to compile answers while providing citations to the sources. This process was iterated 30 times per question to ensure consistency. In this report, we present our observations regarding the retrieval accuracy, consistency of responses, and the challenges encountered during the process. Results Integrating ChatGPT-4 with the 'Link Reader' plug-in demonstrated notable traceability and retrieval accuracy benefits. The AI model successfully provided relevant and accurate clinical answers based on the analyzed guidelines. Despite occasional challenges with webpage access and minor memory drift, the overall performance of the integrated system was promising. The compilation of the answers was also impressive and held significant promise for further trials. Conclusion The findings of this case study contribute to the utilization of AI text-generation models as valuable tools for medical professionals and researchers. The systematic approach employed in this case study and the integration of the 'Link Reader' plug-in offer a framework for automating medical text synthesis, asking one question at a time before compilation from different sources, which has led to improving AI models' traceability and retrieval accuracy. Further advancements and refinement of AI models and integration with other software utilities hold promise for enhancing the utility and applicability of AI-generated recommendations in medicine and scientific academia. These advancements have the potential to drive significant improvements in everyday medical practice