Citations

Background Outcome selection in regional anesthesia and acute pain research is inconsistent, often lacking patient-centered priorities and validated instruments. We aimed to prioritize key outcomes and propose suitable measurement tools to improve the quality, consistency, and relevance of regional anesthesia research. Methods We conducted a multiround Delphi process, including two electronic voting rounds, a multiday in-person meeting, and a patient panel. Experts and patients evaluated existing and proposed outcomes for importance, validity, and relevance. Outcomes reaching ≥70% agreement were prioritized, and appropriate measurement instruments were identified. Results Thirty-two experts and three patients participated. Across three Delphi rounds, 10 outcomes were prioritized for future regional anesthesia research: seven efficacy outcomes (pain scores; opioid consumption; Brief Pain Inventory; functional outcomes; cognitive function; length of stay; block duration); two multidimensional outcomes (quality of recovery and development of a regional anesthesia-specific quality-of-recovery instrument); one safety outcome (chronic postsurgical pain). Patients prioritized cognitive recovery, function, chronic pain, and pain scores. Conclusions This consensus-based, patient-centered framework defines the core priorities for future regional anesthesia and acute pain research. Future work should focus on developing and validating a regional anesthesia-specific quality-of-recovery instrument, refining multidimensional measures such as functional recovery, cognitive outcomes, and the Brief Pain Inventory, and standardizing the reporting of chronic pain and opioid-related outcomes. Adoption of these priorities will enhance methodological consistency, comparability, and patient relevance in future clinical trials.

Abstract Background In the current clinical workflow of radiation oncology, therapists manually summarize the physician‐issued Computed Tomography (CT) simulation orders to prepare patients' CT simulations. This process increases the workload, introduces variability in documentation quality, and is prone to human errors. Purpose This study aims to address these challenges using a large language model (LLM) to automate the generation of summaries from the CT simulation orders and evaluate its performance. Methods A total of 607 CT simulation orders were collected from the Aria database at our institution. A locally hosted LLaMa 3.1 405B model, accessed via the Application Programming Interface (API) service, was used to extract keywords from the CT simulation orders and generate summaries. The downloaded CT simulation orders were categorized into seven groups based on treatment modalities and disease sites. For each group, a customized instruction prompt was developed collaboratively with therapists to guide the LLaMa 3.1 405B model in generating summaries. The ground truth for the corresponding summaries was manually derived by carefully reviewing each CT simulation order and subsequently verified by therapists. The accuracy of the LLM‐generated summaries was evaluated by therapists using the ground truth. Results Over 98% of the LLM‐generated summaries aligned with the ground truth in terms of accuracy. Our evaluations showed an improved consistency in format and enhanced readability from the LLM‐generated summaries compared to the corresponding therapist‐generated summaries. This automated approach demonstrated consistent performance across all groups, regardless of treatment modality or disease site. Conclusions This study demonstrated the high precision and consistency of the LLaMa 3.1 405B model in extracting keywords and summarizing CT simulation orders, suggesting that LLMs have great potential to assist in this task, reduce the workload of CT simulation therapists and improve radiation oncology workflow efficiency.

Background Immune-related adverse events (irAEs) remain largely unpredictable, potentially affecting multiple organ systems and occurring at almost any point during and even occasionally after immune checkpoint inhibitor (ICI) treatment. To identify populations at risk for these immune-mediated toxicities, we analyzed genetic characteristics and immune markers associated with clinically significant irAEs. Methods We carried out a genome-wide association study on 373 white patients receiving ICI treatment. We identified single nucleotide polymorphisms associated with irAEs. Blood cytokine profiling and peripheral blood mononuclear cell RNA sequencing were performed at pretreatment baseline and 6–8 weeks after ICI initiation. Findings were validated in two external cohorts. Results We identified genetic haplotypes in VWA8 (Von Willebrand Factor A Domain Containing 8) , OSBPL6 (Oxysterol Binding Protein Like 6), and ADAMTS9-AS2 (ADAM Metallopeptidase With Thrombospondin Type 1 Motif 9 Antisense RNA 2) associated with grade ≥2 irAEs. Patients carrying risk haplotypes for one or more genes exhibited significantly greater rates of grade ≥2 (OR 3.02; 95% CI 1.83 to 5.02; p<0.001), grade ≥3 (OR 3.59; 95% CI 1.93 to 6.64; p<0.001), and multiple type irAE (OR 2.60; 95% CI 1.53 to 4.39; p<0.001). Serum CCL3 levels were significantly elevated in individuals carrying risk haplotypes (p=0.03). Gene expression analysis demonstrated activated autoimmune and inflammatory pathways in the genetic risk group. Conclusions Novel polymorphisms in VWA8, OSBPL6, and ADAMTS9-AS2 may impact immune pathways, promote inflammation, potentiate autoimmune phenotypes, and convey risk of irAE in ICI-treated patients.

Treatment-induced ovarian function loss is a significant concern for many young patients with breast cancer. Accurately predicting this risk is crucial for counselling young patients and informing their fertility-related decision-making. However, current risk prediction models for treatment-related ovarian function loss have limitations. To provide a broader representation of patient cohorts and improve feature selection, we combined retrospective data from six datasets within the FoRECAsT (Infertility after Cancer Predictor) databank, including 2679 pre-menopausal women diagnosed with breast cancer. This combined dataset presented notable missingness, prompting us to employ cross imputation using the k-nearest neighbours (KNN) machine learning (ML) algorithm. Employing Lasso regression, we developed an ML model to forecast the risk of treatment-related amenorrhea as a surrogate marker of ovarian function loss at 12 months after starting chemotherapy. Our model identified 20 variables significantly associated with risk of developing amenorrhea. Internal validation resulted in an area under the receiver operating characteristic curve (AUC) of 0.820 (95% CI: 0.817–0.823), while external validation with another dataset demonstrated an AUC of 0.743 (95% CI: 0.666–0.818). A cutoff of 0.20 was chosen to achieve higher sensitivity in validation, as false negatives—patients incorrectly classified as likely to regain menses—could miss timely opportunities for fertility preservation if desired. At this threshold, internal validation yielded sensitivity and precision rates of 91.3% and 61.7%, respectively, while external validation showed 92.9% and 60.0%. Leveraging ML methodologies, we not only devised a model for personalised risk prediction of amenorrhea, demonstrating substantial enhancements over existing models but also showcased a robust framework for maximally harnessing available data sources.

Abstract Introduction Obesity, defined as an elevated Body Mass Index (BMI) &gt;30, is a prevalent public health concern with far-reaching implications for various medical procedures and outcomes. In the context of inflatable penile prosthesis placement (IPP), where patient well-being and surgical success are of paramount importance, the impact of obesity on postoperative and intraoperative complications warrants thorough investigation. Objective Our study aimed to investigate the impact of BMI on both postoperative and intraoperative complications in an international, multi-institutional cohort of patients who underwent IPP placement. Methods We performed a retrospective chart review of 4398 cases. These cases were divided into two distinct groups based on BMI: less than 30 (n = 2260) and greater than or equal to 30 (n = 2138). The study assessed various surgical factors as well as complications, including overall complications, corporal scarring, incontinence, intraoperative complications, proximal perforation, distal perforation, proximal crossover, distal crossover, and urethral injury. Results Our analysis revealed that there were no statistically significant differences in intraoperative or postoperative complications between the two BMI categories. Notably, proximal corporal measurement was significantly higher for &gt;30 BMI group (9.55 +/−2.18) compared to &lt;30 BMI group (9.14 +/−2.19), (p = &lt; 0.001). The incidence rates for overall complications (30.2% vs. 28.7%; p = 0.164), corporal scarring (18.2% vs. 16.7%; p = 0.36), incontinence (3.53% vs. 3.13%; p = 0.076), intraoperative complications (1.94% vs. 1.68%; p = 0.253), and specific complications all exhibited no significant differences. Similarly, post-operative complications, including both non- infectious (6.99% vs. 6.22%; p = 0.215) and infectious (2.61% vs. 1.87%; p = 0.165) complications, did not significantly vary between BMI categories. Conclusions Our findings indicate that BMI does not exert a statistically significant influence on postoperative and intraoperative complications in the context of high-volume prosthetic urology. In the absence of any other differences between patients of differing BMIs, surgeons may need to be mindful of pump tubing length concerns in obese patients. Disclosure Any of the authors act as a consultant, employee or shareholder of an industry for: Coloplast, Clarus Therapeutics, Antares Pharma, Acerus.

High-resolution imaging is critical for the diagnosis, treatment planning, and postoperative monitoring of cerebral aneurysms, which affect up to 5% of the population and pose a significant risk of rupture and subarachnoid hemorrhage. Surgical clipping remains a definitive treatment option, but metallic clips can introduce substantial imaging artifacts, complicating posttreatment assessment. This review synthesizes current knowledge on the impact of aneurysm clip materials and designs on artifact generation and explores strategies for artifact mitigation. Conventional materials like titanium are favored for their biocompatibility and reduced ferromagnetism but still cause beam hardening, streak artifacts, and signal loss in CT and MRI scans. Emerging alternatives, including ceramics, composites, polymers, and bioresorbable clips, show promise in reducing artifacts while maintaining mechanical reliability. Innovations in clip design, such as fenestrated or low-profile models, further aid in minimizing imaging distortion. Advanced imaging methods, including dual-energy CT, iterative reconstruction algorithms, and metal artifact reduction software, demonstrate significant improvements in image quality but may introduce limitations such as increased processing demands or subtle anatomical distortions. Future directions emphasize the development of next-generation clip materials, robotic-assisted surgical approaches, and artificial intelligence-driven reconstruction techniques to further optimize visualization and patient safety. Continued research and multidisciplinary collaboration will be essential to translate these innovations into routine neurosurgical practice.

Iatrogenic displacement of third molars is a rare but significant complication of dental extractions, particularly in children where unique anatomic and developmental factors pose additional challenges. This report describes the case of a 12-year-old female who presented with a displaced maxillary third molar crown into the left maxillary sinus following dental extraction. This displacement resulted in acute orbital cellulitis, which was initially managed with oral antibiotic therapy. Endoscopic endonasal retrieval of the foreign body 4 months later was recommended after the initial infection was treated to reduce the risk of recurrent orbital and/or sinonasal infections. Surgical planning incorporated intraoperative navigation with existing cone beam computed tomography to minimize radiation exposure. The procedure was successful with no perioperative complications. This case highlights the importance of prompt recognition, careful surgical planning, and utilization of minimally invasive approaches to optimize outcomes while minimizing craniofacial morbidity in pediatric patients.

ABSTRACT Suicidality contributes substantially to global morbidity and mortality, yet despite its heritability, its biological etiology remains largely elusive. We conducted multi-ancestry genome-wide association study meta-analyses of suicidal ideation (259,747 cases), suicide attempt (64,993 cases), suicide death (9,197 cases), and suicidal behavior (suicide attempt/ death, 75,300 cases), across 54 cohorts (e.g., the Psychiatric Genomics Consortium, Million Veteran Program, UK Biobank). We identified 77 significant loci across meta-analyses, including 59 previously unreported for suicidality. SNP-based heritability ranged from 2.0-6.7% and there were strong, yet incomplete, genetic correlations between suicidality phenotypes (0.70-0.88). Fine-mapping prioritized putative causal SNPs and 20 credible genes. Enrichment analyses implicated synaptic pathways and neuronal populations predominantly in subcortical brain regions (e.g., amygdala excitatory, medium spiny, hippocampal CA1-3). Together, these findings establish suicidality as a polygenic set of traits with both shared and distinct genetic influences, providing a foundation for future studies of suicide biology and etiology.