Pandemic isolation, economic stress, and other stressors may have increased risk for intimate partner violence (IPV) among caregivers raising children. This study examined reported experiences of IPV, associated risk and protective factors, and other correlates among caregivers surveyed as part of a larger study of child and family well-being during the COVID-19 pandemic. We analyzed results from two waves of a nationwide, cross-sectional survey of caregivers of children under the age of 18 during the pandemic (February 2021 and July 2021). The survey collected information from 5,816 participants about their experiences both before and during the pandemic. Of relevance were questions about experiences of IPV in the home, adverse childhood experiences (ACEs), levels of distress, coping mechanisms, employment changes, and parenting behaviors. Close to one quarter (23.5
Artificial intelligence (AI) for surgical workflow analysis often fails to generalize because surgical actions lack a standardized, fine-grained representation. Gesture-level “tokenization” of surgery, capturing instrument–tissue interactions as the smallest intentional functional units, offers greater technical specificity than phase- or step-level labels and has demonstrated associations with proficiency and clinical outcomes. However, the field remains fragmented by heterogeneous gesture terminology, limiting dataset interoperability and model reproducibility. We conducted a SAGES-led, accelerated Delphi consensus process to establish a standardized surgical gesture taxonomy. Starting with 270 literature-derived gesture terms, we employed a novel hybrid pipeline combining large language model (LLM)-assisted semantic clustering with multi-round expert review. The process involved two Delphi surveys (open-ended, then structured agreement) with a predefined ≥ 80
BACKGROUND:Despite the common use of opioids in patients with inflammatory bowel disease (IBD), there are limited studies on the prevalence of chronic opioid use in this population. METHODS:We conducted a nationwide Danish register-based study to examine the prevalence proportion of chronic opioid use in patients with IBD from 1996 to 2021. Analysis was performed for patients with Crohn's disease (CD) and ulcerative colitis (UC) and stratified by sex and age groups of young adults (18-39 years), adults (40-59 years), and elderly (+60 years). RESULTS:A total of 51 837 patients with IBD were identified. The prevalence proportion of chronic opioid use increased from 1996 and reached the highest point at 14.26% (95% confidence interval [CI], 13.67%-14.84%) for patients with CD in 2011 and 8.21% (95% CI, 7.88%-8.54%) in patients with UC in 2012. Subsequently, the prevalence proportion of chronic opioid use decreased to 8.88% (95% CI, 8.41%-9.35%) in patients with CD and 4.96% (95% CI, 4.70%-5.21%) in patients with UC by 2021. Throughout the 25-year period, female patients had higher prevalence proportion of chronic opioid use compared with that of male patients. Elderly patients had higher prevalence proportion of chronic opioid use compared with that of adult and young adult patients. DISCUSSIONS:Many patients with IBD rely on chronic opioid use as part of their pain management. Further investigations are needed to study the complications and sequelae of chronic opioid use in patients with IBD.
Glioblastoma (GBM) remains one of the most lethal adult primary brain tumors, and neurosurgical decision-making increasingly depends on integrating imaging, molecular, perioperative, and post-treatment data. Artificial intelligence (AI) methods have been proposed for several clinically relevant GBM tasks, but the literature remains heterogeneous and difficult to translate into practice. We performed a PROSPERO-registered systematic review of AI, machine learning, and deep learning studies using MRI-derived and/or multimodal perioperative data in GBM for prognosis, risk stratification, treatment-response assessment, post-treatment classification, recurrence/progression prediction, and molecular prediction. Risk of bias was assessed using PROBAST-informed criteria. Thirty studies were included. Survival-focused tasks predominated (20/30, 66.7
Background Prevalent and incident heart failure (HF) and its comorbidities have not been characterized in transgender and gender‐diverse (TGD) populations. This study determines the prevalence and incidence of HF phenotypes by gender identity. Methods A retrospective cohort analysis was performed using deidentified administrative claims from the Optum Labs Data Warehouse (2006–2022). TGD adults and a sample of demographically similar cisgender comparators were identified using a validated algorithm, and gender identity was categorized as cisgender man, cisgender woman, transmasculine, transfeminine, or unclassified TGD. Prevalent and incident HF, including systolic, diastolic, and unclassified phenotypes, were assessed using diagnosis codes. Multivariable logistic and Cox regression models adjusted for age and comorbidities evaluated associations between gender identity and HF. Results In this cohort of 57 471 TGD individuals and 298 213 demographically similar cisgender comparators, 17 898 TGD people (31.1%) were transmasculine, 9652 (16.8%) were transfeminine, and 29 921 (52.1%) had unclassified TGD identity. In adjusted models, transmasculine people experienced less prevalent systolic HF than cisgender men (odds ratio, 0.37 [98.75% CI, 0.17–0.83]) and women (odds ratio, 0.42 [98.75% CI, 0.19–0.92]). Transmasculine people experienced less incident diastolic HF relative to cisgender men (hazard ratio [HR], 0.65 [98.75% CI, 0.51–0.82]) and women (HR, 0.66 [98.75% CI, 0.52–0.83]). Transfeminine people experienced more incident diastolic HF than cisgender men (HR, 1.36 [98.75% CI, 1.05–1.77]) and women (HR, 1.38 [98.75% CI, 1.07–1.79]). Conclusions Prevalent systolic HF and incident diastolic HF differed by gender identity after adjustment for age and clinical comorbidities. Further research is needed to identify underrecognized sex‐ and gender‐based HF risk modifiers.
