Crowdfunding for stroke: a national analysis.

Background: Stroke imposes an enormous economic burden on patients and caregivers. Online crowdfunding is widely used to address healthcare costs, reflecting social safety net gaps, yet it has not been studied for stroke. We performed the first national analysis of stroke-related crowdfunding, evaluating fund totals, success rates, geography, and stroke etiology.

National trends in crowdfunding for neurosurgery: Scalable natural language processing of publicly available data using a large language model.

Objective: Online crowdfunding, commonly used to cover healthcare costs for vulnerable populations, is directly linked to health disparities and gaps in social safety-net systems. The nationwide impact of crowdfunding on neurosurgery remains unclear. We aimed to characterize the funds raised, success rate, geographic distribution, and most frequent conditions for neurosurgery-related crowdfunding campaigns.

Volumetric changes in the bone flap at one year follow up in patients undergoing craniotomy.

Although bone resorption has been extensively reported following craniectomy, bone resorption and fusion rates following craniotomy remains unexplored. The aim of the present study was to conduct a volumetric assessment of craniotomy resorption and fusion rates at one year following the index surgery. Adult patients who had a computed tomography scan immediately after craniotomy and at one year follow up were included in the study.

AtlasGPT: a language model grounded in neurosurgery with domain-specific data and document retrieval.

Objective: Large language models (LLMs) have shown promising performance on medical licensing examinations, but their ability to excel in subspecialty domains and their robustness under adversarial conditions remain unclear. Herein, the authors present AtlasGPT, a subspecialty-focused LLM for neurosurgery, and evaluate its performance on a benchmark multiple-choice question bank and under adversarial testing, as well as its ability to generate high-quality explanations.

Methods: AtlasGPT was built by fine-tuning GPT-4 architecture and retrieval-augmented generation from neurosurgical knowledge sources.

Employing large language models safely and effectively as a practicing neurosurgeon.

Background: Large Language Models (LLMs) have demonstrated significant capabilities to date in working with a neurosurgical knowledge-base and have the potential to enhance neurosurgical practice and education. However, their role in the clinical workspace is still being actively explored. As many neurosurgeons seek to incorporate this technology into their local practice environments, we explore pertinent questions about how to deploy these systems in a safe and efficacious manner.

Practical guide to building machine learning-based clinical prediction models using imbalanced datasets.

Clinical prediction models often aim to predict rare, high-risk events, but building such models requires robust understanding of imbalance datasets and their unique study design considerations. This practical guide highlights foundational prediction model principles for surgeon-data scientists and readers who encounter clinical prediction models, from feature engineering and algorithm selection strategies to model evaluation and design techniques specific to imbalanced datasets. We walk through a clinical example using readable code to highlight important considerations and common pitfalls in developing machine learning-based prediction models.

Predicting Increased Incidence of Common Antibiotic-Resistant and Antibiotic-Associated Pathogens Using Ensemble Species Distribution Modeling.

The Centers for Disease Control estimates antibiotic-associated pathogens result in 2.8 million infections and 38 000 deaths annually in the United States. This study applies species distribution modeling to elucidate the impact of environmental determinants of human infectious disease in an era of rapid global change.

Evidence-based surgery for laparoscopic cholecystectomy.

Background: Laparoscopic cholecystectomy is frequently performed for acute cholecystitis and symptomatic cholelithiasis. Considerable variation in the execution of key steps of the operation remains. We conducted a systematic review of evidence regarding best practices for critical intraoperative steps for laparoscopic cholecystectomy.

Design, construction, and in vivo augmentation of a complex gut microbiome.

Efforts to model the human gut microbiome in mice have led to important insights into the mechanisms of host-microbe interactions. However, the model communities studied to date have been defined or complex, but not both, limiting their utility. Here, we construct and characterize in vitro a defined community of 104 bacterial species composed of the most common taxa from the human gut microbiota (hCom1).

Scalable deep learning algorithm to compute percent pulmonary contusion among patients with rib fractures.

Background: Pulmonary contusion exists along a spectrum of severity, yet is commonly binarily classified as present or absent. We aimed to develop a deep learning algorithm to automate percent pulmonary contusion computation and exemplify how transfer learning could facilitate large-scale validation. We hypothesized that our deep learning algorithm could automate percent pulmonary contusion computation and that greater percent contusion would be associated with higher odds of adverse inpatient outcomes among patients with rib fractures.

Influence of Socioeconomic and Environmental Determinants of Health on Human Infection and Colonization with Antibiotic-Resistant and Antibiotic-Associated Pathogens: A Scoping Review.

Antibiotic-resistant and antibiotic-associated pathogens are commonly encountered by surgeons. Pathogens such as methicillin-resistant (MRSA), infection (CDI), and carbapenem-resistant (CRE) result in considerable human morbidity, mortality, and excess healthcare expenditure. Human colonization or infection can result from exposure to these pathogens across a range of domains both inside and outside of the built healthcare environment, exposure that may be influenced by socioeconomic and environmental determinants of health, the importance of which has not been investigated fully.

Practical Computer Vision Application to Compute Total Body Surface Area Burn: Reappraising a Fundamental Burn Injury Formula in the Modern Era.

Importance: Critical burn management decisions rely on accurate percent total body surface area (%TBSA) burn estimation. Existing %TBSA burn estimation models (eg, Lund-Browder chart and rule of nines) were derived from a linear formula and a limited number of individuals a century ago and do not reflect the range of body habitus of the modern population.

Objective: To develop a practical %TBSA burn estimation tool that accounts for exact burn injury pattern, sex, and body habitus.

Efficacy of intercostal cryoneurolysis as an analgesic adjunct for chest wall pain after surgery or trauma: systematic review.

Background: There is a critical need for non-narcotic analgesic adjuncts in the treatment of thoracic pain. We evaluated the efficacy of intercostal cryoneurolysis as an analgesic adjunct for chest wall pain, specifically addressing the applicability of intercostal cryoneurolysis for pain control after chest wall trauma.

Methods: A systematic review was performed through searches of PubMed, EMBASE, and the Cochrane Library.

A new approach to biomining: Bioengineering surfaces for metal recovery from aqueous solutions.

Electronics waste production has been fueled by economic growth and the demand for faster, more efficient consumer electronics. The glass and metals in end-of-life electronics components can be reused or recycled; however, conventional extraction methods rely on energy-intensive processes that are inefficient when applied to recycling e-waste that contains mixed materials and small amounts of metals. To make e-waste recycling economically viable and competitive with obtaining raw materials, recovery methods that lower the cost of metal reclamation and minimize environmental impact need to be developed.