Citations

Introduction Implementation science research increases the uptake of evidence-based interventions, which may improve health equity among racial and ethnic minorities. However, it is unclear how anti-racism and anti-colonialism practices have been integrated into implementation science research. The objectives of this scoping review are to describe the current conceptualisations of racism and colonialism within the USA, examine racism or colonialism-conscious approaches and analyse gaps in the operationalisation of anti-racism or anti-colonialism within implementation science studies. Methods and analysis This scoping review will be conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for scoping reviews guidelines. The Center for Chronic Disease Reduction and Equity Promotion Across Minnesota conceptual framework and an implementation science anti-racism lens will guide the study design and analysis. To determine study eligibility for the scoping review, articles will undergo abstract and full-text screening by two independent reviewers and discrepancies will be settled together. Data charting will be extracted from included articles by eight independent reviewers. The search strategy will use controlled vocabulary and natural language keywords related to health equity, health disparities and anti-racism/colonialism on six databases. The scoping review will include studies that applied implementation science theories, models or frameworks among US-based populations. Additionally, included studies will report any of the following implementation activities: implementation strategies, implementation outcomes, adaptations to evidence-based interventions, or evaluations of pre-implementation or implementation context. Ethics and dissemination No ethical approval was required for the scoping review. Dissemination will be through publication in a peer-reviewed journal and conference presentations.

Background: The PRO-CTCAE provides patient-reported data on symptomatic AEs. A summary metric—the ACS—reflecting total AE burden can be calculated by averaging AE-level composite scores at a given timepoint for each participant. This study investigated the psychometric properties and interpretability of this PRO-CTCAE ACS in patients with breast, lung, or head/neck cancers. Methods: We conducted a secondary analysis of a PRO-CTCAE validation dataset comprising 940 adults undergoing chemotherapy or radiation therapy (clinicaltrials.gov: NCT02158637). We focused on empirically recommended symptom terms for three cancer sites. Analyses included Spearman’s correlations, coefficient alpha, and eigenvalues from the correlation matrices, confirmatory factor analysis (CFA), and principal component analysis (PCA). Latent profile analysis (LPA) was used to assess ACS interpretability in the lung cohort. Results: Mean composite score inter-correlations were moderate (0.30–0.35), and coefficient alphas were high (0.81–0.91). Eigenvalue ratios and CFA supported retention of a single factor/component, with suitable model fit indices. ACS correlated highly with factor scores and the first principal component from the PCA. Reduced sets of terms produced reliable scores that closely approximated the full set scores and aligned with external criteria. LPA in the lung subgroup identified four latent classes; ACS differentiated high vs. low symptom burden groups but did not distinguish the two groups expressing distinct symptom profiles. Conclusion: The ACS demonstrated structural validity through adequately fitting linear factor models and effectively summarized symptomatic AE burden. However, similar ACS values may mask clinically distinct symptomatic AE profiles, underscoring the value of both summary metrics and profile-based approaches.

Abstract The highest risk for liver allograft rejection is within the first weeks to months after a liver transplant; however, while later-stage mechanisms are well described, the initial window at the moment of alloantigen influx (e.g., ~ 1 h post-reperfusion) remains unmapped. T-cell priming is initiated in lymph nodes, yet these sites are rarely profiled in humans and their relationship to rejection risk is unclear. To address this gap, we profiled paired pre- and ~ 1 h post-reperfusion hepatic draining lymph nodes from liver transplant recipients using multiplexed immunofluorescence and GeoMx whole-transcriptome profiling to identify features that predict rejection versus non-rejection. Early after reperfusion, CD4 + and CD8 + T cells became proximal to macrophages in rejection compared to dendritic cells in non-rejection, which also displayed an immunoregulatory-leaning profile marked by PD-1, IDO1, and CD39, suggesting that macrophages and dendritic cells differentially influence early T-cell alloimmunity. These results provide nodal targets for precise immune modulation.

Artificial intelligence (AI), including machine learning, deep learning, reinforcement learning, and generative AI, has the potential to advance critical care nephrology (CCN) by enhancing prediction accuracy, improving diagnostic capabilities, supporting clinical decision making, and streamlining workflow processes. Current applications in CCN include AKI prediction, nephrotoxin surveillance, intradialytic hypotension forecasting, and AI-guided dialysis and continuous KRT management, with performance often exceeding traditional models. However, the effect on patient-centered outcomes such as mortality, dialysis dependence, and cost-effectiveness remains uncertain. Emerging techniques, such as conformal prediction for calibrated risk estimates, causal inference for intervention modeling, and reinforcement learning for adaptive ultrafiltration, show promise in enhancing reliability, interpretability, and individualized care. Generative AI and large language models extend these applications to clinical documentation, reasoning, and patient education, while raising new challenges, including hallucinations, regulatory oversight, and clinician trust. Persistent barriers such as data heterogeneity, limited external validation, alert fatigue, and economic constraints hinder broad adoption. This review synthesizes the current evidence and outlines four priorities for advancing AI in CCN: ( 1 ) rigorous multicenter validation focused on clinical outcomes, ( 2 ) integration of uncertainty quantification and causal modeling into AI tools, ( 3 ) development of clinician-centered interfaces that minimize cognitive load, and ( 4 ) establishment of transparent, adaptive regulatory and governance frameworks. Realizing the promise of AI in CCN will require multidisciplinary collaboration, fairness and generalizability testing, and sustainable implementation strategies that align technologic innovation with measurable improvements in patient care.

INTRODUCTION: Indomethacin is often used experimentally to induce intestinal hyperpermeability, enabling evaluation of interventions targeting barrier function. METHODS: We conducted a randomized, double-blind, placebo-controlled study (NCT05538247) in healthy volunteers to assess whether a supplement could mitigate indomethacin-induced hyperpermeability. Participants received 150 mg/d of indomethacin for 6 days, either before or during placebo/supplement administration. Permeability was measured using 13 C-mannitol and lactulose urinary excretion. RESULTS: Contrary to expectations, indomethacin failed to increase 13 C-mannitol excretion in either group. No meaningful elevations in serum (zonulin, claudins) or fecal (calprotectin) biomarkers were observed. DISCUSSION: Our findings suggest that the expected increase in intestinal permeability after indomethacin administration may not be consistently observed in healthy volunteers. These results highlight the need to carefully consider the reproducibility and sensitivity of this model in future clinical studies aiming to investigate gut barrier function.

Objective Patients with chronic illness share their experiences in online communities and generate rich data on pain management. This study applied natural language processing methods, including large language models (LLMs), to Reddit discussions from lupus communities to characterize multidimensional pain experiences framed in the biopsychosocial model. Methods We extracted Reddit posts from the r/Lupus and r/LupusSupport subreddits posted from June 9, 2010, through December 31, 2023. Pain‐related posts were identified using a clinically informed pain lexicon. Topic modeling was used to identify thematic patterns, which were then compared with structured summaries generated by LLM instructions that were fine‐tuned using the biopsychosocial model of pain. Two reviewers conducted content analysis of the LLM‐generated summaries, evaluating thematic accuracy and coverage. Results Data from Reddit included 31,785 posts from 10,857 authors. We identified common pain complaints, management strategies, and sociocultural, affective, and nociplastic dimensions of pain. Instruction fine‐tuned LLMs produced structured summaries with an average thematic accuracy score of 3.1 of 4 (kappa = .09) and content coverage score of 2.9 of 4 (kappa = .38). Sociocultural features presented in 123 posts (33.8%), including peer support and validation (n = 106) and provider interactions or access issues (n = 35). Nociplastic pain presented in 205 posts (56.3%). Conclusion Natural language processing methods can be used to extract rich, multidimensional insights into pain experiences from online communities focused on lupus. These approaches highlight the psychological, social, and cultural facets of pain that may be underrepresented in clinical settings, supporting more patient‐centered approaches to care in rheumatology.

Objective Cognitive impairment is prevalent in rheumatoid arthritis (RA), yet risk factors are not well understood. We explored associations between clinical characteristics and cognitive impairment in an RA cohort. Methods Data were from a longitudinal RA cohort at Veterans Affairs Puget Sound Health Care System. Cognition was evaluated using the Saint Louis University Mental Status (SLUMS) examination. Demographics and health factors, objectively measured physical function, participant‐reported symptoms, RA disease characteristics, and comorbidities were evaluated. Univariable linear regressions explored the association between clinical factors and SLUMS scores. Those with P < 0.1 in the univariable models were evaluated in separate multivariable linear regressions controlling for age, sex, and years of education. Results A total of 145 participants with RA were included, with a mean age of 64.5 (SD 11.6) years, and were predominantly male (74%). Using SLUMS, 42 participants (29%) had normal cognition, 83 (57%) had mild cognitive impairment, and 20 (14%) had dementia. Physical performance (aβ: 0.35, confidence interval [CI] 0.11 to 0.59), self‐reported exhaustion (aβ: −2.22, CI −3.44 to −0.99), pain (aβ: −0.25, CI −0.50 to −0.00), disability (aβ: −1.79, CI −3.16 to −0.42), and trouble falling asleep (aβ: −2.57, CI −4.00 to −1.14) were all independently associated with a lower SLUMS score (all P < 0.05). Conclusion Cognitive impairment was prevalent in our cohort of veterans with RA and was associated with several modifiable clinical factors. Future longitudinal studies are needed to determine the directionality of these associations and evaluate interventions for modifiable risk factors that may mitigate cognitive dysfunction. image

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.