Third-window anomalies represent a heterogeneous group of inner ear disorders characterized by abnormal openings in the bony otic capsule, resulting in altered cochlear and vestibular fluid mechanics. By creating an alternative pathway for inner ear fluid motion due to a new opening in addition to the oval and round windows, these lesions disrupt normal sound transmission and vestibular function. In pediatric patients, recognition of third-window anomalies is particularly important, as early and accurate diagnosis supports appropriate clinical assessment and management. High-resolution computed tomography of the temporal bone is the primary imaging modality for evaluating third-window anomalies, enabling detailed assessment of the bony labyrinth. However, many of these abnormalities are subtle and may be underestimated or misinterpreted on standard axial images. The use of anatomy-oriented multiplanar reconstructions tailored to the suspected defect significantly improves lesion conspicuity and diagnostic confidence. This educational review summarizes the anatomic and pathophysiologic principles underlying third-window phenomena in children and illustrates the imaging features of major entities, including semicircular canal dehiscence, enlarged vestibular aqueduct, and less common cochlear dehiscence. The aim is to provide a clear and practical imaging-based framework to support radiologists involved in the evaluation of pediatric inner ear abnormalities.
Background: Platinum resistance remains a major therapeutic challenge in epithelial ovarian cancer (EOC). The tumour–stroma ratio (TSR) has emerged as a potential prognostic biomarker in several malignancies; however, its predictive value for platinum resistance in EOC remains unclear. Methods: This retrospective cohort study included 83 patients with EOC who underwent primary debulking surgery (PDS) or neoadjuvant chemotherapy followed by interval debulking surgery (NACT + IDS) between January 2017 and January 2024. TSR was assessed on haematoxylin and eosin–stained sections and classified as low (< 50%) or high (≥ 50%). The primary endpoint was platinum resistance. Secondary endpoints included overall survival (OS) and disease-free survival (DFS). Survival outcomes were analysed using the Kaplan–Meier method. Results: Platinum resistance was observed in 26.5% of patients. In the PDS cohort, TSR was not significantly associated with clinicopathological characteristics, platinum resistance, OS, or DFS. In contrast, in the NACT + IDS cohort, high TSR was significantly associated with platinum resistance (75% vs. 25%, p = 0.032). No significant differences in OS or DFS were detected according to TSR status in either treatment group, although patients with high TSR in the NACT + IDS cohort tended to show poorer survival outcomes. Conclusions: High TSR is associated with platinum resistance in patients with EOC treated with neoadjuvant chemotherapy. TSR may represent a simple and accessible predictive biomarker for identifying patients at increased risk of treatment resistance. Further validation in larger, prospective, multicentre studies is warranted.
Background: Unplanned out-of-hospital birth (OOHB) is associated with increased maternal and neonatal morbidity, largely due to limited access to professional obstetric support. In this context, leaving the placenta in situ in the prehospital setting may influence outcomes and represents an important clinical question. Objective: To evaluate the association between the site of placental delivery (in-hospital vs. out-of-hospital) and maternal and perinatal outcomes among women with unplanned out-of-hospital births. Methods: In this retrospective cohort study, 34,724 deliveries between January 1, 2015 and January 31, 2025 were screened, and 197 eligible cases were included in the analysis. Cases were stratified according to the site of placental delivery into two groups: in-hospital placental delivery and out-of-hospital placental delivery. Demographic, obstetric, maternal, and neonatal variables were compared in accordance with the STROBE reporting guideline. Results: Placental delivery occurred out of hospital in 62.9% of cases. The out-of-hospital placental delivery group had a significantly greater postpartum hemoglobin decline (1.77 ± 0.64 g/dL vs. 1.59 ± 0.95 g/dL; p < 0.001). The overall maternal complication rate was higher in the in-hospital placental delivery group (24.7% vs. 12.1%; p = 0.020), suggesting potential referral/presentation bias toward more complex or unstable cases. There were no significant between-group differences in perinatal outcomes (stillbirth, congenital anomalies, or total neonatal complications). However, NICU admission was more frequent in the in-hospital group (39.7% vs. 24.2%; p = 0.017). After adjustment for key confounders, out-of-hospital placental delivery remained associated with lower odds of any maternal complication and NICU admission, whereas the difference in hemoglobin decline was no longer statistically significant. Conclusions: Out-of-hospital placental delivery was associated with greater maternal blood loss, likely reflecting suboptimal third-stage management in the prehospital setting. Nevertheless, perinatal outcomes appeared to be driven largely by the overall circumstances of delivery. Leaving the placenta in situ may serve as a short-term stabilization strategy in resource-limited emergencies; however, active management of the third stage of labor should be initiated promptly upon hospital arrival.
Purpose Artificial intelligence (AI) is increasingly being applied in the field of infectious diseases, yet the global trajectory of this research domain has not been comprehensively characterized. This study aimed to map the evolution of scientific output on AI applications in infectious diseases over the decade 2016–2025, characterizing thematic clusters, collaboration networks, and emerging research trends to guide future clinical investigations. Methods A systematic bibliometric analysis was performed using the Web of Science Core Collection (WoSCC). The query combined AI-related terms (machine learning, deep learning, neural networks, large language models, and allied concepts) restricted to the WoSCC category of "Infectious Diseases" and an English-language filter, yielding 1,252 original articles published between January 2016 and December 2025. Bibliometric computations and visualizations were conducted using the Bibliometrix R package, VOSviewer, and Scimago Graphica. Keyword co-occurrence network analysis and temporal trend mapping were employed to identify thematic clusters and emerging research foci. Results The 1,252 articles were contributed by 9,160 authors from 124 countries across 2,850 institutions and published in 125 journals. Annual output grew more than 30-fold between 2016 and 2025, with 84.7% of all publications appearing in the final five years. The United States and China were the most productive nations and collectively dominated international collaboration networks. Harvard University was the leading institution. Keyword network analysis identified nine distinct thematic clusters. Recent trend analyses reveal a significant shift towards clinical applications, specifically highlighting antimicrobial resistance (AMR) surveillance and early sepsis prediction as dominant research hotspots. Conclusion Scientific production on AI applications in infectious diseases has expanded exponentially over the past decade, catalyzed principally by the COVID-19 pandemic. Sepsis management and AMR represent the most prominent areas for future investigation. These findings indicate that AI has a growing potential to become an integral component of infectious disease practice.
Gestational diabetes mellitus (GDM) is an increasingly common pregnancy complication associated with adverse maternal–fetal outcomes and long-term risk of type 2 diabetes. Artificial intelligence (AI) tools such as ChatGPT have emerged as potential instruments for patient education, yet their reliability and safety in obstetric counseling remain underexplored. This study aimed to evaluate the educational quality of ChatGPT-5 responses to frequently asked questions about GDM. This cross-sectional study was conducted between January and June 2025 at İzmir Tepecik Training and Research Hospital, Türkiye, following STROBE guidelines. Twenty frequently asked questions about gestational diabetes mellitus, identified from routine antenatal counseling through clinician observation and consensus, were entered into ChatGPT-5 using a standardized prompt. Thirty physicians specializing in obstetrics and gynecology from Türkiye, the United Kingdom, Canada, and Germany independently rated the AI-generated answers on a 5-point Likert scale across five domains: accuracy, comprehensiveness, clarity, safety, and appropriateness. Reliability was analyzed using Cronbach’s α and intraclass correlation coefficient (ICC). Comparative analyses were performed using Friedman and Dunn–Bonferroni tests. Overall, ChatGPT-5 achieved high ratings for accuracy (4.23 ± 0.32), clarity (4.18 ± 0.27), and appropriateness (4.09 ± 0.31), while comprehensiveness (3.92 ± 0.29) and safety (3.88 ± 0.34) scored lower. Internal consistency was strong (Cronbach’s α = 0.87) and interrater reliability was good (ICC = 0.79). Among thematic domains, “Definition, causes, and risk factors” received the highest scores (4.20 ± 0.30), whereas “Follow-up and treatment” scored the lowest (3.96 ± 0.33). Safety ratings were significantly lower than accuracy and clarity (p = 0.018). ChatGPT-5 demonstrated high accuracy and clarity in GDM counseling, suggesting its potential use as a physician-supervised educational support tool. However, limitations in comprehensiveness and safety highlight the need for guideline-based refinement and human oversight to ensure ethical and reliable integration of AI in obstetric patient education. Not applicable. This study is an observational cross-sectional design and did not involve patient enrollment or intervention.
