Croghan, Ivana T.

Objectives Long-term symptoms following infection with acute respiratory syndrome coronavirus-2 (SARS-CoV-2), termed post-acute sequelae of SARS-CoV-2 infection (PASC) or Long COVID (LC), persist beyond 90 days and may impact at least 10% of patients with acute infection. The exact cause of LC remains unknown, but several theories include persistent viral particles, immune dysfunction, and autonomic nervous system (ANS) imbalance, particularly involving the vagus nerve. Given the role of the vagus nerve in inflammation regulation, vagal nerve stimulation (VNS) is being explored as a treatment option. The present study aimed to evaluate the feasibility, safety, and exploratory effects of a handheld external transcutaneous cervical non-invasive VNS (tcVNS) on the primary endpoint of Post-COVID Functional Status (PCFS) grade, alongside secondary outcomes such as LC-related fatigue and other symptoms. Methods Participants from our Post COVID Care Clinic were randomized into treatment (tcVNS treatments two minutes, three times a day) and control (no VNS) groups for a study period of 12 weeks. Participants completed validated questionnaires, PET-CT scans, and inflammatory cytokines at baseline and 12 weeks. Results All randomized patients (8 tcVNS, 10 control) completed the 12-week study. Participants randomized to tcVNS were observed to have improvements in PHQ-9, PROMIS-F, and COMPASS-31 when compared to controls, but these differences were not statistically significant. Cortisol and DHEA were also observed to increase less in the tcVNS group when compared to the control group, but these changes were also not statistically significant. Conclusions tcVNS was well tolerated and may be a promising therapeutic approach for LC, a chronic disease with no approved treatments and very low rates of complete recovery.

Conventional cigarette smoking remains a leading preventable cause of morbidity and mortality despite declining prevalence, yet evidence-based treatments remain underused. This narrative review examines guideline-supported therapies and emerging approaches, including digital tools, neuromodulation, and harm-reduction strategies. Standard care typically combines FDA-approved pharmacotherapy with behavioral counseling. Digital interventions can extend access and support adherence, particularly when integrated with pharmacotherapy and follow-up. Repetitive transcranial magnetic stimulation (rTMS) can be considered as an adjunctive option for patients who do not respond to pharmacotherapy and is FDA-approved. For patients unable to quit, structured harm-reduction strategies may help reduce risk while supporting progression toward eventual abstinence. These approaches should be used alongside, not in place of, guideline-recommended pharmacotherapy and behavioral counseling, which remain the gold standard for care. This review outlines a pragmatic, primary care–oriented model that integrates stepped-care with harm-reduction strategies.

Primary care is a language-intensive environment where clinicians frequently synthesize, document, and communicate large amounts of information. Given the amount of information in primary care, large language models (LLMs) have emerged as tools to support primary care clinician workflows, aiming to reduce administrative burden and augment patient care. LLMs are artificial intelligence tools that can generate text and assist with chart summarization, message drafting, and clinical decision support. However, because LLMs are probabilistic, identical inputs may yield inconsistent responses. The LLMs output can be significantly improved through prompt engineering. Prompt engineering is the intentional design of text entered into the LLM to help produce better LLM results. Prompting is best approached as an iterative process where a clinician defines the task, the LLM generates output, and the clinician critically reviews and refines the results. Low-quality prompts are often vague and produce generic or misleading responses. High-quality prompts are specific, context-rich, and well-structured. Using structured prompting frameworks can improve the LLMs response. Including core elements, such as the LLMs role, the clinical context, the requested task, and the desired output formatting, can help improve the LLMs response by providing situational background and framing response formatting. Additional frameworks, such as Ask-Context-Expectation and PICO+O, can be utilized for specific patient circumstances and cases. However, despite their promise, LLMs have important limitations. They may generate inaccurate or hallucinated information and produce inconsistent results. Given these limitations, LLMs should not be viewed as knowledge authorities but rather as cognitive assistants. Thoughtful prompt design can help reduce these limitations and improve response accuracy, always keeping in mind that LLMs should not replace human judgment or clinical reasoning. As LLMs continue to be integrated into primary care, prompt engineering will become an increasingly important clinical skill. Careful prompt design and critical appraisal of LLM responses will be key to maintaining excellent patient care in the AI era.

Objective To evaluate patient satisfaction with the experience of using an autonomous wheelchair to transport patients in a large outpatient clinical environment. Methods The autonomous wheelchair pilot was approved as a feasibility pilot by the institutional committees and deemed a quality improvement project by the Institutional Review Board (IRB). A total of 409 adult patients using an autonomous wheelchair at a large academic medical center who volunteered to complete a paper survey were included. The survey was administered immediately after autonomous wheelchair use, using a cross-sectional, anonymous survey, between 15 Oct 2025 and 14 Jan 2026. Of 409 completed surveys, six were excluded because participants did not identify their endpoint for stratification purposes. Descriptive analysis included frequencies and percentages of responses. Results No collisions or adverse events were observed during the pilot, and the system operated reliably within the predefined routes. Most survey respondents were first-time users (335/402 [83.3%]). A majority reported they would use the autonomous wheelchair again (341/395 [86.3%]) and would recommend it to others (364/397 [91.7%]). Overall, the experience was rated better than expected by 293 of 393 participants (74.6%). When given a choice, 271 of 379 respondents (71.5%) preferred the autonomous wheelchair over a staff-operated wheelchair. Conclusion These findings suggest that autonomous wheelchairs are feasible and acceptable to patients in a controlled outpatient setting and support continued piloting and prospective evaluation.

Objective Long COVID (LC) refers to the long-term symptoms occurring after SARS-CoV-2 infection and resolution of the initial disease prodrome. While LC is increasingly recognized, knowledge about its biomarkers is still limited. This study examines imaging findings in 18 F-FDG PET-CT scans in 40 patients with LC from an academic LC Clinic, for potential imaging biomarkers. Methods This study included patients aged 18 and older with confirmed positive SARS-CoV-2 PCR test and at least 28 days post-symptom onset. 18 F-FDG PET-CT scans were performed, and brain metabolic activity was compared to a control database to generate Z-scores. Participants were grouped based on their clinical phenotype and Z-scores of different brain regions were analyzed using t-tests. Results The study group was mainly female (70%), with a median age of 53 years, and predominantly non-Hispanic White (90%). Most had pre-existing conditions such as gastrointestinal, cardiovascular, endocrine, and psychiatric disorders. The findings showed significant cerebral hypometabolism in 29 patients with fatigue and post-exertional malaise (PEM), particularly in the left sensorimotor cortex (p=0.0253) and bilateral primary visual cortex (right: p=0.0096, left: p=0.0016), which persisted up to two years after infection. Conclusions In summary, this study identified persistent cerebral hypometabolism in LC patients, especially those with fatigue with PEM, up to two years post-infection. These results suggest that 18 F-FDG PET-CT could be a valuable tool for diagnosing and managing LC. Further research is essential to confirm these findings and improve treatment strategies for patients with LC.

10578 Background: Multicancer detection (MCD) testing using cell-free DNA methylation patterns is a novel blood-based approach to cancer screening. In 2022, Mayo Clinic implemented offering MCD testing into routine clinical practice. Prior real-world reports have been limited to single-site experiences. We evaluated enterprise-wide implementation of MCD testing across all three Mayo Clinic sites using the Mayo Clinic Platform. Methods: We performed a retrospective analysis of de-identified clinical data from Mayo Clinic Platform Discover, including GRAIL Galleri tests performed at Mayo Clinic sites in Rochester, MN; Scottsdale, AZ; and Jacksonville, FL. This activity did not require IRB review per 45 CFR 46.102. Demographics, test results, cancer diagnoses, and prior cancer history were extracted using a privacy-preserving protocol, with data de-identified by expert determination. ICD-10 diagnostic codes were used to identify subsequent cancer diagnoses. Results: A total of 8,201 Galleri tests were performed in 7,300 patients; some patients underwent repeat testing. Fifty-seven tests (0.7%) were positive. Among MCD-positive patients, cancer diagnostic codes were identified in 32 patients, yielding a positive predictive value (PPV) of 58.7%. The most common cancers identified were oropharyngeal (34.4%), lymphoid and hematopoietic (34.4%), and gastrointestinal malignancies (18.8%).Among 8,144 MCD-negative tests (99.3% of all testing performed), 423 patients (5.2%) subsequently received a cancer diagnostic code, yielding a negative predictive value of 92.5%. The most commonly identified cancers in this group were male genital organs (36.6%), urinary tract (10.6%), and thyroid or other endocrine glands (7.3%). Overall, 10.1% of patients had a prior cancer history, which was more common in MCD-positive than MCD-negative patients (22.6% vs 10.0%). Patients with positive tests were older (mean age 68.4 vs 61.5 years) and more often male (67% vs 62%). Race distribution was similar. Conclusions: Enterprise-wide implementation of MCD testing across diverse clinical settings is feasible within routine workflows. Over half of positive tests were associated with cancer diagnostic codes, supporting the clinical promise of MCD testing. Limitations include incomplete capture of externally diagnosed cancers and lack of cancer signal-of-origin data. The Mayo Clinic Platform enables scalable, secure analytics to support future cancer interception strategies.

Purpose While the clinical utility for Point-of-Care Ultrasound (POCUS) is well-established through its use in clinical practice to enhance diagnostic accuracy, expedite care, and improve patient interactions/communication, its impact on patient experience in outpatient consultative settings remains largely underexplored. We sought to evaluate patients’ experience and satisfaction by incorporating POCUS use during outpatient consultations. Methods In this prospective, cross-sectional survey study conducted in a consultative outpatient internal medicine practice, a continuous sample of 100 patients undergoing POCUS during routine medical evaluations participated. We assessed patient perspectives using a survey that was adapted from validated instruments. The following items were evaluated using the anonymized survey: the extent of patient understanding, the integration of POCUS into the consultation, its impact on patient-doctor communication, perceived quality of care, and overall patient experience. Results Most respondents felt better informed about their health (88%), more thoroughly examined (94%), and reported improved confidence in their physician’s assessment (92%) after the POCUS exam. In addition, POCUS was perceived as a natural part of the consultation (95%) and minimally burdensome (93%). Overall, 96% of study respondents agreed that POCUS improved their quality of care, and 98% described their experience as positive or very positive. Open-ended comments emphasized the immediacy of results, enhanced communication, and cost-effectiveness of the POCUS experience. Conclusion Incorporating POCUS in this environment improved patients’ perceptions of their physician, clinic, and overall care. These findings support the broader adoption of POCUS in similar clinical settings and its potential impact on healthcare delivery and patient satisfaction.

Background: Artificial intelligence (AI) is increasingly being integrated into healthcare, with applications ranging from predictive analytics to clinical decision support. In clinical nutrition, AI tools offer opportunities to improve workflow efficiency, enhance dietary assessment, and personalize nutrition care. Despite growing interest, little is known about registered dietitian nutritionists’ (RDNs) perceptions of AI in clinical practice. The aim of the present study was to assess RDNs’ attitudes toward AI use within a large healthcare system, along with their perceived barriers in this regard. Methods: A cross-sectional survey was developed through expert review and distributed electronically via REDCap to RDNs across Mayo Clinic’s academic campuses and affiliated health system sites. The 23-item survey included Likert-scale items addressing AI’s potential utilization within clinical care, perceived benefits and risks, and readiness for adoption. Responses were summarized using descriptive statistics. Factor analysis identified underlying constructs related to AI attitudes. Differences stratified by age and years of experience were evaluated using ANOVA. Results: Of the 185 RDNs invited, 64 (35%) responded. Two factors emerged: optimism regarding AI usage (Cronbach’s α = 0.94) and skepticism about implementation (α = 0.76). The overall mean ± SD score for optimism was 0.1 ± 0.6 (neutral), while skepticism averaged 1.0 ± 0.6 (moderate). Skepticism differed by years of experience (p = 0.012), with the lowest levels observed among RDNs with ≥21 years of practice. No significant differences were observed across age groups. Discussion: RDNs demonstrated neutral attitudes toward AI use but expressed concerns about accuracy, training, and implementation challenges. Addressing these concerns through education and structured implementation strategies may facilitate successful adoption of AI in dietetic practice.

Abstract Background Clinical nutrition (CN) is becoming increasingly complex because of the rising prevalence of chronic illness, cancer, and malnutrition‐related conditions such as short bowel syndrome and refeeding syndrome. Despite its clinical significance, nutrition education among US physicians remains limited. Simultaneously, large language model (LLM)–based artificial intelligence assistants (AIAs) have emerged as tools to support complex clinical decision‐making but remain largely untested in CN. Methods This retrospective study evaluated four LLM‐based AIAs—ChatGPT (OpenAI), OpenEvidence (OpenEvidence Inc), Gemini (Google, Google DeepMind), and Copilot (Microsoft Corporation)—using five complex CN cases from our nutrition support service. Each AIA was queried with patient‐specific CN questions. Responses were blinded and reviewed by five physician CN experts using an eight‐item assessment tool evaluating clarity, relevance, evidence, and clinical utility. Results All AIAs produced clinically appropriate responses, with Gemini scoring highest in relevance (4.04) and clarity (4.16). Overall satisfaction scores ranged from 3.08 (Copilot) to 3.84 (Gemini). Citation quality and originality of insights varied and were generally limited, and no consistent differences in performance were observed across the five cases among the four AIAs. Conclusion LLM‐based AIAs can reliably replicate expert reasoning in CN. Although not yet a source of novel clinical insights, the true potential of this approach may lie in its application among physicians without specialized expertise in CN, helping to bridge existing knowledge gaps in nutrition care. Presenting full clinical cases, as shown in this study, could support AIA‐enabled e‐consultation in the future, thereby addressing gaps in CN education.

Background: Chronic nonspecific low back pain (CNLBP) is a prevalent, disabling condition worldwide. Tuina, a traditional Chinese manual therapy, has shown potential benefits, but evidence remains limited. Objective: This study evaluated the effectiveness and safety of Tuina therapy in managing CNLBP in a U.S. population. Methods: A prospective, single-blind, randomized controlled trial was conducted at Mayo Clinic Rochester campus (February 2020-April 2024). 204 participants with CNLBP were randomized into 3 groups: Tuina (N = 68), physical therapy (N = 68), or a combination of both (N = 68). Each group received 6 treatments over 2 months. The primary outcome was pain intensity (VAS); secondary outcomes included disability (ODI), quality of life (SF-36), and adverse events. Additionally, participants completed a satisfaction survey at the end of the trial. Research Electronic Data Capture (REDCap) was used for survey data collection. Data was assessed by analysis of variance (ANOVA) for the multiple repeated measurements, Kruskal-Wallis tests, while Chi-square tests, and Bonferroni correction in SAS software. Results: 185 subjects completed the study. Significant decreases in VAS, ODI, and SF-36 Overall Physical scores were found from baseline, to post treatment and follow-up in all groups ( P < .05), while there were no significant differences in VAS, ODI, or SF-36 outcomes between the Tuina group and the other 2 groups. There were 4 adverse events observed in the trial, 1 in the Physical therapy group and 3 in the Mixed group. Only 1 transient low back pain after Tuina therapy was observed in the Mixed group but did not require treatment. The high percentage of patients who received the Tuina program was extremely helpful (n = 94/126, 77.0%). The willingness to recommend was significantly higher for Tuina compared to physical therapy (Mixed group [N = 59/62, 95.2%], Tuina group [N = 57/64, 91.9%], and physical therapy group [N = 31/59, 59.6%] ( P < .05). Conclusion: Tuina therapy showed comparable benefits to physical therapy and combined treatment of both in improving pain, function, and quality of life in patients with CNLBP. With high patient satisfaction and a favorable safety profile, Tuina appears to be a safe and acceptable non-invasive treatment option. Further studies are needed to validate these findings in the future.