Chronic sialorrhea is a condition characterized by excessive drooling, often associated with neurological and neuromuscular disorders such as Parkinson’s disease, cerebral palsy, and stroke. Despite its prevalence, it remains underdiagnosed and poorly understood, leading to a lack of comprehensive data on patient demographics, clinical characteristics, and treatment patterns. This study aimed to help fill these existing gaps by analyzing real-world data using Optum’s de-identified Clinformatics^® Data Mart Database. Patients were required to have a diagnosis indicative of sialorrhea plus evidence of sialorrhea treatment between 1/1/2007 and 5/31/2022. Two cohorts were analyzed: patients with evidence of newly diagnosed sialorrhea and associated treatment, and sialorrhea patients initiating incobotulinumtoxinA. Clinical characteristics, comorbidities, symptoms, and treatment utilization were described before and after diagnosis and incobotulinumtoxinA initiation. No formal statistical comparisons were performed. Patients were predominantly aged 65 or older, male, and non-Hispanic white. Parkinson’s disease and cerebral palsy were the most common comorbidities among adults and children, respectively. Treatment patterns suggest that anticholinergics are more commonly used than botulinum toxin therapy. The findings offer valuable information for improving diagnosis and treatment approaches and suggest a need for further research into treatment effectiveness, safety, and disease burden.

Total knee arthroplasty (TKA) is the final treatment option for patients with advanced knee osteoarthritis (OA). Unfortunately, TKA surgery is accompanied by acute postoperative pain that is more severe than arthroplasty performed in other joints. Elucidating the molecular mechanisms specific to post-TKA pain necessitates an animal model that replicates clinical TKA procedures, induces acute postoperative pain, and leads to complete functional recovery. Here, we present a new preclinical TKA model in rats and report on functional and behavioral outcomes indicative of pain, analgesic efficacy, serum cytokine levels, and dorsal root ganglia (DRG) transcriptomes during the acute postoperative period. Following TKA, rats exhibited marked deficits in weight bearing that persisted for 28 days. Home cage locomotion, rearing, and gait were similarly impacted and recovered by day 14. Cytokine levels were elevated on postoperative days one and/or two. Treatment with morphine, ketorolac, or their combination improved weight bearing while gabapentin lacked efficacy. When TKA was performed in rats with OA, similar functional deficits and comparable recovery time courses were observed. Analysis of DRG transcriptomes revealed upregulation of transcripts linked to multiple molecular pathways including inflammation, MAPK signaling, and cytokine signaling and production. In summary, we developed a clinically relevant rat TKA model characterized by resolution of pain and functional recovery within five weeks and with pain-associated behavioral deficits that are partially alleviated by clinically administered analgesics, mirroring the postoperative experience of TKA patients.

ChatGPT and other Large Language Models (LLMs) are not only being more readily integrated into healthcare but are also being utilized more frequently by patients to answer health-related questions. Given the increased utilization for this purpose, it is essential to evaluate and study the consistency and reliability of artificial intelligence (AI) responses. Low back pain (LBP) remains one of the most frequently seen chief complaints in primary care and interventional pain management offices. This study assesses the readability, accuracy, and overall utility of ChatGPT's ability to address patients' questions concerning low back pain. Our aim is to use clinician feedback to analyze ChatGPT's responses to these common low back pain related questions, as in the future, AI will undoubtedly play a role in triaging patients prior to seeing a physician. To assess AI responses, we generated a standardized list of 25 questions concerning low back pain that were split into five categories including diagnosis, seeking a medical professional, treatment, self-treatment, and physical therapy. We explored the influence of how a prompt is worded on ChatGPT by asking questions from a 4th grader to a college/reference level. One board certified interventional pain specialist, one interventional pain fellow, and one emergency medicine resident reviewed ChatGPT's generated answers to assess accuracy and clinical utility. Readability and comprehensibility were evaluated using the Flesch-Kincaid Grade Level Scale. Statistical analysis was performed to analyze differences in readability scores, word count, and response complexity. How a question is phrased influences accuracy in statistically significant ways. Over-simplification of queries (e.g. to a 4th grade level) degrades ChatGPT's ability to return clinically complete responses. In contrast, reference and neutral queries preserve accuracy without additional engineering. Regardless of how the question is phrased, ChatGPT's default register trends towards technical language. Readability remains substantially misaligned with health literacy standards. Verbosity correlates with prompt type, but not necessarily accuracy. Word count is an unreliable proxy for informational completeness or clinical correctness in AI outputs and most errors stem from omission, not commission. Importantly, ChatGPT does not frequently generate false claims. This analysis complicates the assumption that “simpler is better” in prompting LLMs for clinical education. Whereas earlier work in structured conditions suggested that plain-language prompts improved accuracy, our findings indicate that a moderate reading level, not maximal simplicity, yields the most reliable outputs in complex domains like pain. This study further supports that AI LLMs can be integrated into a clinical workflow, possibly through electronic health record (EHR) software.