Autoimmune disease not associated with monoclonal gammopathy

Embargoed for release until 5:00 p.m. ET on Monday 20 May 2024    
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Below please find summaries of new articles that will be published in the next issue of Annals of Internal Medicine. The summaries are not intended to substitute for the full articles as a source of information. This information is under strict embargo and by taking it into possession, media representatives are committing to the terms of the embargo not only on their own behalf, but also on behalf of the organization they represent.    
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1. Autoimmune disease not associated with monoclonal gammopathy 

Previous studies may have been affected by ascertainment bias 

Abstract: https://www.acpjournals.org/doi/10.7326/M23-2867   

URL goes live when the embargo lifts      

A study of more than 75,000 persons screened for monoclonal gammopathy of undetermined significance (MGUS) found no association between the presence of autoimmune diseases and MGUS. According to the authors, these findings are clinically significant because they indicate that recommendations to routinely screen patients with autoimmune disease for MGUS may not be warranted. The findings are published in Annals of Internal Medicine. 

 

Researchers from the University of Iceland analyzed a cross-sectional study where 75,422 adults were screened for MGUS to see if they could find an association between MGUS and autoimmune diseases. Of those screened, 10,818 had an autoimmune disease, and 599 of the patients with autoimmune diseases had MGUS. There were no differences between organ-specific and systemic autoimmune diseases or between antibody-positive and antibody-negative disorders. The authors determined that the diagnosis of an autoimmune diseases was not associated with MGUS, but it was associated with a prior clinical diagnosis of MGUS. These findings suggest that previously observed associations between autoimmune conditions and MGUS may have been affected by ascertainment bias. The authors caution that studies based only on clinical MGUS cohorts that are very likely to have been identified through work-up of the same diseases with which an association is being sought has led to a false association. 

 

Media contacts: For an embargoed PDF, please contact Angela Collom at acollom@acponline.org. To speak with the corresponding author, Ingigerdur Sverrisdottir, MD, please contact ingigerdur.sverrisdottir@vgregion.se.

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2. Definitions and models of care for long COVID remain unclear

Abstract: https://www.acpjournals.org/doi/10.7326/M24-0677  

URL goes live when the embargo lifts     

A scoping review of 38 published articles related to the definition and care of long COVID found differences between reported definitions and models of care for the condition. The authors say that a standardized, valid, and reliable definition is needed to accurately identify patients with the condition and to develop and study the potential benefit of various long COVID models of care. The review is published in Annals of Internal Medicine.

 

Researchers from Oregon Health & Science University studied reviews describing long COVID definitions or models of care to provide an overview of care models, including a proposed framework to describe and distinguish models. The authors found that five clinical definitions of long COVID varied with regard to timing since symptom onset and the minimum duration required for diagnosis; 1 additional definition was symptom score–based. The authors also evaluated the research to create a framework of care models. They identified 10 representative practice-based and 3 systems-based models of care using this method. In addition, many of the models managed care for either adults or children, and managed care for uninsured or underinsured patients but were not designed to do so. They note that given the high demand for long COVID care, models that increase capacity are a high priority. They also note that although integration of long COVID management into primary care is a potential strategy to increase capacity, challenges include the need to ensure appropriate training, increase primary care engagement, and ensure sufficient staff and specialist support.

 

Media contacts: For an embargoed PDF, please contact Angela Collom at acollom@acponline.org. To speak with the corresponding author, Roger Chou, MD, please contact chour@ohsu.edu

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3. AI tool useful but not a replacement for human screening of the literature for systematic reviews 

Abstract: https://www.acpjournals.org/doi/10.7326/M23-3389   

Editorial: https://www.acpjournals.org/doi/10.7326/M24-0877   

URL goes live when the embargo lifts      

An analysis of the AI tool GPT 3.5 Turbo found that it may be used as a second reviewer for title and abstract screening and may reduce the number of citations before screening by humans at the cost of creating additional work by adding false positives and missing citations. The analysis is published in Annals of Internal Medicine.  

Researchers from the Centre for Research in Epidemiology and Statistics (CRESS) conducted an analysis to investigate the sensitivity and specificity of GPT-3.5 Turbo, as a single reviewer, for title and abstract screening in systematic reviews. The authors developed a framework to guide the model in its screening process using 5 prompts evaluating a different component of the PICOS (Population, Intervention, Comparison, Outcomes, and Study design) framework. The model screened 5 systematic reviews representing 22,665 citations. They found that the current performance of GPT-3.5 Turbo models is insufficient to fully replace manual screening in systematic reviews but it could be used to assist reviewers to help them deal with uncertainties and could be used to reduce the number of citations before title and abstract screening by humans. However, according to the authors, use of these models is currently limited by their lower specificities compared with human reviewers, the fact that performance depends on the prompts and therefore the need for prompt engineering, and the limited reproducibility of outputs. 

The authors of an editorial from the University of Colorado and Annals of Internal Medicine cautions that time efficiencies gained through GPT may be offset by the time invested in prompt engineering and the reconciliation of false positives. They also note that the limited number of reviews included in this study may make it difficult to determine what characteristics may affect performance. They advise several avenues for future research on the use of GPT in research processes.   

Media contacts: For an embargoed PDF, please contact Angela Collom at acollom@acponline.org. To speak with the corresponding author, Viet-Thi Tran, MD, PhD, please contact thi.tran-viet@aphp.fr.

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