Citations

Abstract Artificial intelligence (AI) encompasses the development of systems that can perform human‐like tasks, such as treatment guidance, decision‐making, pattern recognition and understanding language. Within AI, machine learning and deep learning play pivotal roles in diagnosis and outcome prediction, while natural language processing aids in synthesising large datasets from the electronic medical record. In orthopaedics, AI has demonstrated success in various areas, including image evaluation, surgical planning, outcome prediction, cohort identification and administrative tasks. The purpose of this manuscript was to provide an overview of the benefits of AI implementation within the field of orthopaedics. An additional goal was to address the challenges associated with producing high quality AI‐based research in a rapidly developing field. Level of Evidence Level IV.

Abstract Glioblastoma is the most common type of malignant primary brain tumor and a major cause of morbidity and mortality. In 2021, the World Health Organization updated the classification of Central Nervous System (CNS) tumors to restrict glioblastomas to isocitrate dehydrogenase-wildtype (IDHwt) tumors, improving understanding of the prognosis and optimal therapy for these tumors. This revision also enables more homogeneous populations of patients to be enrolled in clinical trials, facilitating the evaluation of novel therapies. In this updated consensus review from the Society for Neuro-Oncology (SNO) and the European Association of Neuro-Oncology (EANO), the current management of patients with glioblastoma is discussed. In addition, novel therapies such as immunotherapies, viral therapies, targeted molecular therapies, theranostics, and antibody–drug conjugates will be reviewed, as well as the current challenges and future directions for research.

Background Given the low haemorrhagic risk of intracranial low-grade dural arteriovenous fistulas (dAVFs), the benefits of routine intervention remain controversial. This study compares patient outcomes treated with stereotactic radiosurgery (SRS) versus conservative management. Method Multicentre retrospective analysis of the Consortium for Dural Arteriovenous Fistula Outcomes Research and the International Radiosurgery Research Foundation data. Inclusion criteria were (1) intracranial low-grade dAVF diagnosed by catheter-based angiography, (2) no prior dAVF-related haemorrhage and (3) management with upfront SRS (intervention group) or conservative management (observation group). The primary outcome was symptomatic improvement. Secondary outcomes included dAVF obliteration, up-conversion, haemorrhage, improvement and favourable modified Rankin Scale (mRS) at follow-up. Results 304 patients with a mean age of 56 years (SD 13.5) and a follow-up of 46.7 months (SD 45.5) were included. 135 (44.4%) were managed conservatively and 169 (55.6%) had upfront SRS. Compared with the observation group, symptomatic and mRS Score improvement (≥1-point decrease in baseline score) was more likely in the intervention group (95.1% vs 58.5%; OR=13.75 (5.61–33.69) and 37.0% vs 24.0%; OR=1.85 (1.09–3.15), respectively). These findings remained significant after multiple imputation and propensity score matching. Remaining outcomes were similar between groups. The all-cause mortality rate was 5.4% (n=16), unrelated to the dAVF or treatment. Five (3.0%) SRS-related complications were reported and resolved during the follow-up period. Conclusions SRS was associated with increased symptomatic and mRS Score improvement for low-grade dAVFs compared with conservative management. SRS had a low complication risk and did not appear to alter dAVF obliteration or haemorrhage. Future prospective trials on SRS as a first-line intervention for symptomatic low-grade dAVFs should be considered.

AbstractArtificial intelligence (AI) has been influencing healthcare and medical research for several years and will likely become indispensable in the near future. AI is intended to support healthcare professionals to make the healthcare system more efficient and ultimately improve patient outcomes. Despite the numerous benefits of AI systems, significant concerns remain. Errors in AI systems can pose serious risks to human health, underscoring the critical need for safety, as well as adherence to ethical and moral standards, before these technologies can be integrated into clinical practice. To address these challenges, the development, certification, and deployment of medical AI systems must adhere to strict and transparent regulations. The European Commission has already established a regulatory framework for AI systems by enacting the European Union Artificial Intelligence Act. This review article, part of an AI learning series, discusses key considerations for medical AI systems such as reliability, accuracy, trustworthiness, lawfulness and legal compliance, ethical and moral alignment, sustainability, and regulatory oversight.Level of EvidenceLevel V.

Abstract Background The Global Leadership Initiative on Malnutrition (GLIM) introduced an approach for malnutrition diagnosis in 2019 that comprised screening followed by assessment of three phenotypic criteria (weight loss, low body mass index [BMI], and low muscle mass) and two etiologic criteria (reduced food intake/assimilation and inflammation/disease burden). This planned update reconsiders the GLIM framework based on published knowledge and experience over the past 5 years. Methods A working group ( n = 43 members) conducted a literature search spanning 2019–2024 using the keywords “Global Leadership Initiative on Malnutrition or GLIM.” Prior GLIM guidance activities for using the criteria on muscle mass and inflammation were reviewed. Successive rounds of revision and review were used to achieve consensus. Results More than 400 scientific reports were published in peer‐reviewed journals, forming the basis of 10 systematic reviews, some including meta‐analyses of GLIM validity that indicate strong construct and predictive validity. Limitations and future priorities are discussed. Working group findings suggest that assessment of low muscle mass should be guided by experience and available technological resources. Clinical judgment may suffice to evaluate the inflammation/disease burden etiologic criterion. No revisions of the weight loss, low BMI, or reduced food intake/assimilation criteria are suggested. After two rounds of review and revision, the working group secured 100% agreement with the conclusions reported in the 5‐year update. Conclusion Ongoing initiatives target priorities that include malnutrition risk screening procedures, GLIM adaptation to the intensive care setting, assessment in support of the reduced food intake/assimilation criterion, and determination of malnutrition in obesity.

Abstract Humanity is facing an important existential threat—irreversible climate change caused by human activity. Until recently, most of the proposals to address climate change have downplayed or ignored the adverse impact of food systems, especially intensive animal agriculture. This is in spite of the fact that up to a third of global greenhouse gas production to date can be attributed to animal agriculture. Recent developments at COP28 have signaled that the tide is turning, however, and that food systems are becoming part of global discussions on climate change solutions. The pressing nature of irreversible climate change requires rethinking our food systems. To solve the climate change crisis, we propose transitioning to a predominantly plant-based diet, and phasing out intensive animal agriculture as diets shift, without increasing pastoral farming. We suggest that such transformations in global food systems can be accomplished largely through education and large-scale public information campaigns, removal of subsidies, taxation to account for externalized costs of animal agriculture, improved labelling of products, and various investment/divestment drivers. Better metrics and industry benchmarks involving food and agriculture-specific performance indicators that reflect food system sustainability will be important. Increased global awareness of these issues and a change in mindset (which will drive political will) also are needed. Our current trajectory is untenable, and we must begin to turn the ship now towards sustainable food systems and diets.

Abstract The vision of the ASPEN Rhoads Research Foundation is to promote innovation and research discovery to improve nutrition care and health. The Foundation was launched in 1993, incorporated in 2002, and established as a nonprofit 501(c)(3) organization in 2007. On the occasion of American Society for Parenteral and Enteral Nutrition (ASPEN)'s 50th anniversary, this article examines the creation of the Foundation, the state of clinical nutrition research funding, the Foundation's philanthropic mission, and the return on investment of Foundation grant support. To date, 114 investigators have received 2.78 million dollars in funding. The article examines the success of ASPEN research programs and Foundation grant support in career development, leadership, and mentorship. It is important to document a historical account of the ASPEN Rhoads Research Foundation and encourage innovation and discovery and ongoing financial support.

AbstractArtificial intelligence's (AI) accelerating progress demands rigorous evaluation standards to ensure safe, effective integration into healthcare's high‐stakes decisions. As AI increasingly enables prediction, analysis and judgement capabilities relevant to medicine, proper evaluation and interpretation are indispensable. Erroneous AI could endanger patients; thus, developing, validating and deploying medical AI demands adhering to strict, transparent standards centred on safety, ethics and responsible oversight. Core considerations include assessing performance on diverse real‐world data, collaborating with domain experts, confirming model reliability and limitations, and advancing interpretability. Thoughtful selection of evaluation metrics suited to the clinical context along with testing on diverse data sets representing different populations improves generalisability. Partnering software engineers, data scientists and medical practitioners ground assessment in real needs. Journals must uphold reporting standards matching AI's societal impacts. With rigorous, holistic evaluation frameworks, AI can progress towards expanding healthcare access and quality.Level of EvidenceLevel V.

OBJECTIVE Borden-Shucart type I dural arteriovenous fistulas (dAVFs) lack cortical venous drainage and occasionally necessitate intervention depending on patient symptoms. Conversion is the rare transformation of a low-grade dAVF to a higher grade. Factors associated with increased risk of dAVF conversion to a higher grade are poorly understood. The authors hypothesized that partial treatment of type I dAVFs is an independent risk factor for conversion. METHODS The multicenter Consortium for Dural Arteriovenous Fistula Outcomes Research database was used to perform a retrospective analysis of all patients with type I dAVFs. RESULTS Three hundred fifty-eight (33.2%) of 1077 patients had type I dAVFs. Of those 358 patients, 206 received endovascular treatment and 131 were not treated. Two (2.2%) of 91 patients receiving partial endovascular treatment for a low-grade dAVF experienced conversion to a higher grade, 2 (1.5%) of 131 who were not treated experienced conversion, and none (0%) of 115 patients who received complete endovascular treatment experienced dAVF conversion. The majority of converted dAVFs localized to the transverse-sigmoid sinus and all received embolization as part of their treatment. CONCLUSIONS Partial treatment of type I dAVFs does not appear to be significantly associated with conversion to a higher grade.