Background: Robot-assisted hysterectomy (RAH) has been progressively introduced in gynecologic surgery, yet nationwide data describing its uptake and early efficiency outcomes remain limited. We described trends in surgical route for hysterectomy within France and evaluate the length of hospital stay (LoS) according to surgical route and center expertise in robot-assisted surgery (RAS). Methods: All total hysterectomies (excluding vaginal) performed in France between January 2020 and December 2024 were identified from the national Programme de Médicalisation des Systèmes d’Informations (PMSI) registry. Procedures were categorized as open, laparoscopic hysterectomy (LH), or RAH and stratified by indication (endometriosis, other benign conditions, malignancy) and by center expertise in RAS, including multidisciplinary RAS centers. Primary outcomes were surgical volumes by approach and LoS. Results: Among 196,050 hysterectomies, LH remained the predominant approach; nevertheless, the proportion of RAH increased steadily across all indications over time, mainly at the expense of open surgery. This increase was more pronounced in high-volume and multidisciplinary RAS centers. Across indications, LoS was consistently shorter after minimally invasive surgery than after open hysterectomy. LoS following RAH was comparable to LH and decreased progressively over time. In experienced and multidisciplinary RAS centers, RAH was associated with the lowest LoS for benign indications. Conclusions: These nationwide, real-world findings show that RAH is increasingly being integrated into minimally invasive hysterectomy pathways within France, with LoS comparable to laparoscopy and shorter than open surgery. Our results support the role of RAS expertise and multidisciplinary organization in optimizing early clinical and operational outcomes during the adoption of gynecologic RAS.
Large language models (LLMs) are used in all areas of life and have become one of the information sources for those seeking healthcare. Although ChatGPT is the most well-known, Claude, CoPilot, and GEMINI are also among the other LLMs. Some of these models have been studied in terms of their response quality metrics to frequently asked questions (FAQs) about broad content areas like anesthesia and to specific FAQs related to obstetric analgesia. However, no studies have yet been conducted on questions related to nerve blocks. In this study, we evaluated the quality of the answers given by the four LLMs to frequently asked questions related to ‘nerve block’. Prospective, Delphi study, Survey. Ten FAQs were identified and presented to four LLMs. A Delphi study was conducted to develop an assessment tool. A survey study was then conducted using the developed tool, in which the evaluators, selected through a thorough process, evaluated the LLM responses. The quality of LLM responses was assessed by raters using the ARQuAT (Assessing Response Quality in AI Texts) tool, determined through Delphi rounds. Evaluation criteria included content criteria such as accuracy, comprehensiveness, security, timeliness, and relevance, as well as communication criteria such as understandability, empathy, ethical considerations, readability, and neutrality. ChatGPT and Claude demonstrated superior performance in ARQuAT-Overall scores compared to GEMINI and CoPilot (p < 0.001). ChatGPT and Claude achieved satisfaction rates above 80
Pontine diffuse midline gliomas (PDMGs) are among the most lethal pediatric brain tumors with a median survival of approximately one year. Reliable prognostic markers are needed to guide treatment strategies and inform families. Our study aims to investigate the prognostic utility of delta-radiomic features in PDMGs. We retrospectively analyzed 35 pediatric patients with PDMG diagnosed between 2012 and 2020, all treated with radiotherapy plus concomitant and adjuvant temozolomide. Pre- and post-treatment MRI (T1-weighted, T2-weighted, and ADC maps) were subjected to manual segmentation and texture feature extraction using MaZda software. Delta radiomics features were calculated as ratios between post- and pre-treatment values. Diagnostic performance for predicting survival below or above 12 months was assessed using ROC analysis, while overall survival was further evaluated with Kaplan–Meier and Cox regression models. Median overall survival was 13 months (range: 6–69). Among baseline features, only sum average from non-contrast T1 was significant, with lower values (indicating higher heterogeneity) associated with shorter OS. In the post-radiotherapy setting, sum entropy from T1 images emerged as an independent prognostic predictor, with higher values correlating with longer OS. Delta radiomics parameters, particularly sum entropy derived from T1 and T2 sequences, yielded higher AUC values than single post-treatment features, suggesting superior prognostic accuracy. Our preliminary results indicate that delta-radiomics outperforms static texture analysis in predicting overall survival in pediatric PDMGs, and to the best of our knowledge, this is the first study to investigate the prognostic utility of delta-radiomics in this population. Evidence is provided that delta radiomics can serve as a non-invasive marker for early prognostic stratification in pediatric PDMGs. Validation in larger, multi-center cohorts is required to confirm its clinical utility.
Postoperative complications continue to be a principal cause of morbidity following surgeries for colorectal cancer. This retrospective cohort study comprising 296 patients who underwent elective resections between 2014 and 2018 examined the influence of perioperative hyperglycemia, nutritional recovery, and the duration of diabetes on postoperative outcomes. Blood glucose levels were documented across the preoperative, intraoperative, and postoperative periods and categorized into four ranges (< 70, 70–119, 120–180, > 180 mg/dL). Nutritional recovery was characterized by the time to initial oral intake and resumption of a regular diet. Complications were classified using the Clavien–Dindo system, occurring in 14.5
