Pilot Study of Large Language Models as an Age-Appropriate Explanatory Tool for Chronic Pediatric Conditions.

There exists a gap in existing patient education resources for children with chronic conditions. This pilot study assesses large language models' (LLMs) capacity to deliver developmentally appropriate explanations of chronic conditions to pediatric patients. Two commonly used LLMs generated responses that accurately, appropriately, and effectively communicate complex medical information, making them a potentially valuable tool for enhancing patient understanding and engagement in clinical settings.

Are short term outcomes superior following total knee arthroplasty when infra-patellar fat pad is resected? A systematic review and meta-analysis.

BACKGROUND: Data suggests the infra-patellar fat pad (IPFP) is resected in the majority of total knee arthroplasty cases. Current literature suggests there may be scope for increasing preservations rates. A systematic review and meta-analysis of available literature was performed to assess if resection of IPFP resulted in superior patient outcomes. METHODS: Scopus, PubMed, Cochrane, Embase and CINAHL were systematically searched in February 2024 for articles of relevance and meta-analysis conducted. A standardized mean difference and confidence interval of 95% was calculated. An odds ratio was calculated for all included datasets. Heterogeneity was assessed for using the I2 statistic. RESULTS: Thirteen studies were included within this review. IPFP resection shortened the patella tendon at 6 months post-operatively (OR, 0.07; 95% CI, 0.02-0.12, P = 0.006). Results favoured preservation at 12 months post-operatively (OR, 0.02; 95% CI, -0.02 to 0.06, P = 0.32). Oxford Knee Society (OKS) results statistically favoured preservation at 6 months (OR, 1.57; 95% CI, 0.74-2.39, P = 0.0002). Findings at 12 months favoured resection (OR, -0.49; 95% CI, -5.39, 4.41, P = 0.84). Resection increased anterior knee pain at 6-12 months post-operatively (OR, 1.45; 95% CI, 1.12-1.89, P = 0.005). Combined subgroup analysis of flexion favoured resection (OR, -2.15; 95% CI, -6.52 to 2.22, P = 0.34). CONCLUSION: OKS and patella tendon length results favoured preservation at 6 months however did not reach minimal clinically important difference. Combined subgroup analysis of flexion did not yield a statistically significant result. Rates of anterior knee pain at 6-12 months favoured preservation.

Proactive Polypharmacy Management Using Large Language Models: Opportunities to Enhance Geriatric Care.

Polypharmacy remains an important challenge for patients with extensive medical complexity. Given the primary care shortage and the increasing aging population, effective polypharmacy management is crucial to manage the increasing burden of care. The capacity of large language model (LLM)-based artificial intelligence to aid in polypharmacy management has yet to be evaluated. Here, we evaluate ChatGPT's performance in polypharmacy management via its deprescribing decisions in standardized clinical vignettes. We inputted several clinical vignettes originally from a study of general practicioners' deprescribing decisions into ChatGPT 3.5, a publicly available LLM, and evaluated its capacity for yes/no binary deprescribing decisions as well as list-based prompts in which the model was prompted to choose which of several medications to deprescribe. We recorded ChatGPT responses to yes/no binary deprescribing prompts and the number and types of medications deprescribed. In yes/no binary deprescribing decisions, ChatGPT universally recommended deprescribing medications regardless of ADL status in patients with no overlying CVD history; in patients with CVD history, ChatGPT's answers varied by technical replicate. Total number of medications deprescribed ranged from 2.67 to 3.67 (out of 7) and did not vary with CVD status, but increased linearly with severity of ADL impairment. Among medication types, ChatGPT preferentially deprescribed pain medications. ChatGPT's deprescribing decisions vary along the axes of ADL status, CVD history, and medication type, indicating some concordance of internal logic between general practitioners and the model. These results indicate that specifically trained LLMs may provide useful clinical support in polypharmacy management for primary care physicians.

Empathy and Equity: Key Considerations for Large Language Model Adoption in Health Care.

The growing presence of large language models (LLMs) in health care applications holds significant promise for innovative advancements in patient care. However, concerns about ethical implications and potential biases have been raised by various stakeholders. Here, we evaluate the ethics of LLMs in medicine along 2 key axes: empathy and equity. We outline the importance of these factors in novel models of care and develop frameworks for addressing these alongside LLM deployment.

Natural history of Ebola virus disease in rhesus monkeys shows viral variant emergence dynamics and tissue-specific host responses.

Ebola virus (EBOV) causes Ebola virus disease (EVD), marked by severe hemorrhagic fever; however, the mechanisms underlying the disease remain unclear. To assess the molecular basis of EVD across time, we performed RNA sequencing on 17 tissues from a natural history study of 21 rhesus monkeys, developing new methods to characterize host-pathogen dynamics. We identified alterations in host gene expression with previously unknown tissue-specific changes, including downregulation of genes related to tissue connectivity. EBOV was widely disseminated throughout the body; using a new, broadly applicable deconvolution method, we found that viral load correlated with increased monocyte presence. Patterns of viral variation between tissues differentiated primary infections from compartmentalized infections, and several variants impacted viral fitness in a EBOV/Kikwit minigenome system, suggesting that functionally significant variants can emerge during early infection. This comprehensive portrait of host-pathogen dynamics in EVD illuminates new features of pathogenesis and establishes resources to study other emerging pathogens.