Explore the vast range of titles available.

What is this summary about? Gout is a painful inflammatory form of arthritis that happens when urate levels build up in the blood for a long time. This causes urate crystals to form in the joints, tendons, and soft tissues, causing swelling and pain. Some patients have “uncontrolled gout” when conventional, oral medications do not work well or cause side effects. Pegloticase is an intravenously administered treatment for uncontrolled gout. It uses a modified enzyme to change urate into a substance called allantoin, which the body can easily remove via the kidneys. Patients take pegloticase with a medicine called methotrexate, which prevents their immune systems from making pegloticase ineffective. The MIRROR study investigated how well pegloticase worked with and without methotrexate in treating uncontrolled gout and how safe it was when used for a year. Afterward, the number of patients who went into remission, meaning their gout was well controlled, was investigated. There were two definitions of remission: one was more complex with six criteria and the other was simpler with three criteria. After 1 year of pegloticase treatment, 43% of patients met the complex definition and 70% met the simpler definition. These results show that pegloticase helped many people with long-term uncontrolled gout reach remission. The results also show that the simpler definition of uncontrolled gout is a practical way for doctors to see how well treatments work in managing uncontrolled gout. What were the results? Using the six-criteria remission definition (adapted from de Lautour 2016), 43% of included patients achieved gout remission after 52 weeks of pegloticase treatment. Using the three-criteria remission definition (adapted from G-CAN), 70% of included patients achieved gout remission after 52 weeks of pegloticase treatment. What do the results mean? All patients in the MIRROR RCT had uncontrolled gout, and patients had experienced gout for an average of approximately 14 years. These analyses showed that lowering serum urate levels with pegloticase for 12 months put gout into remission in a large proportion of these patients with hard-to-treat disease. Importantly, these findings indicate that the simplified criteria adapted from the G-CAN definition were practical to use in routine clinical care and could be used to see how well treatment is working in patients with uncontrolled gout.

The increasing volume and complexity of biomedical literature pose challenges for making scientific knowledge accessible to lay audiences. Lay summaries, now widely encouraged or required by journals, aim to bridge this gap by promoting health literacy, patient engagement, and public trust. However, many are written by scientists without formal training in plain-language communication, often resulting in limited clarity, readability, and consistency. Generative large language models such as ChatGPT-4 offer a scalable opportunity to support lay summary creation, though their effectiveness within specific clinical domains has not been systematically evaluated at scale. This study aimed to assess ChatGPT-4's performance in generating lay summaries for prostate cancer studies. A secondary objective was to evaluate how prompt design influences summary quality, aiming to provide practical guidance for the use of generative artificial intelligence (AI) in scientific publishing. A total of 204 consecutive articles on prostate cancer were extracted from a high-ranking oncology journal mandating lay summaries. Each abstract was processed with ChatGPT-4 using 2 prompts: a simple prompt based on the journal's guidelines and an extended prompt refined to improve readability. AI-generated and original summaries were evaluated using 3 criteria: readability (Flesch-Kincaid Reading Ease [FKRE]), factual accuracy (5-point Likert scale, blinded rating by 2 clinical experts), and compliance with word count instructions (120-150 words). Summaries were classified as high-quality as a composite outcome if they met all 3 benchmarks: FKRE >30, accuracy ≥4 from both raters, and word count within range. Statistical comparisons used Wilcoxon signed-rank and paired 2-tailed t tests (P<.05). ChatGPT-4-generated lay summaries showed an improvement in readability compared to human-written versions, with the extended prompt achieving higher scores than the simple prompt (median FKRE: extended prompt 47, IQR 42-56; simple prompt 36, IQR 29-43; original 20, IQR 9.5-29; P<.001). Factual accuracy was higher for the AI-generated lay summaries compared to originals (median factual accuracy score: extended prompt 5, IQR 5-5; simple prompt 5, IQR 5-5; original 5, IQR 4-5; P<.001) in this dataset. Compliance with word count instructions was greater for both AI-generated summaries in comparison to originals (wrong number of words; extended prompt 39 (19%), simple prompt 40 (20%), original 140 (69%); P<.001). Between simple and extended prompts, there were no significant differences in accuracy (P=.53) and word count compliance (P=.87). The proportion rated as high-quality was 79.4% for the extended prompt, 54.9% for the simple prompt, and 5.4% for original summaries (P<.001). With optimized prompting, ChatGPT-4 produced lay summaries that, on average, scored higher than author-written versions in readability, factual accuracy, and structural compliance within our dataset. These results support integrating generative AI into editorial workflows to improve science communication for nonexpert audiences. Limitations include focus on a single clinical domain and journal, and absence of layperson evaluation.

Summary What is this summary about? Systemic sclerosis (SSc) is a condition that affects the immune system (the body’s natural defence system) and causes the skin to harden and thicken in large patches. Research shows that 30% to 90% of people with SSc also have interstitial lung disease (ILD), a condition that causes inflammation and scarring of the lungs. When people have SSc and ILD, it is known as SSc-associated ILD or SSc-ILD. The authors of this plain language summary of publication (PLS-P) reviewed different articles to find out what the key issues were in the way doctors and patients with SSc-ILD communicate with each other. What were the results? The key messages from the studies were: Most patients felt uneasy when they were diagnosed with SSc-ILD Good communication between doctors and patients at the first visit is crucial as it sets the tone for future relationships Both doctors and patients avoid talking about how SSc-ILD symptoms may get worse (prognosis) or the subject of death. Patients should be encouraged to ask questions to address important and personal topics that would not be talked about otherwise Patients may feel intimidated by a doctor, which could interfere with communication Doctors must be able to listen and show empathy to build a relationship with patients and be aware that different communication styles may suit a patient during different stages in their journey Doctors should avoid using a lot of technical terms. Patients felt metaphors helped them understand their condition better Patients have different awareness, thoughts, and feelings about SSc-ILD than doctors. If doctors understand this, it may improve the communication between doctors and patients Ways to close the gap between the way doctors and patients communicate include patients having the opportunity to access: Self-learning and patient organizations Peer-mentoring (patients mentoring other patients) Information technology Shared decision-making, where the doctor and patient work together to come to a decision about treatment and care What do the results mean? The best way to improve the feelings patients have when they are diagnosed with SSc, including SSc-ILD, is to improve the quality of the communication between doctors and patients. The quality of the first meeting between a doctor and patient sets the tone for future checkups, especially if the doctor can listen, show empathy, and allow the patient to ask questions. Improving the patient’s knowledge about SSc-ILD, for example by using websites, reading printed materials, or taking part in peer-mentoring schemes, may also contribute to a better conversation.

The idea of making science more accessible to nonscientists has prompted health researchers to involve patients and the public more actively in their research. This sometimes involves writing a plain language summary (PLS), a short summary intended to make research findings accessible to nonspecialists. However, whether PLSs satisfy the basic requirements of accessible language is unclear. We aimed to assess the readability and level of jargon in the PLSs of research funded by the largest national clinical research funder in Europe, the United Kingdom's National Institute for Health and Care Research (NIHR). We also aimed to assess whether readability and jargon were influenced by internal and external characteristics of research projects. We downloaded the PLSs of all NIHR National Journals Library reports from mid-2014 to mid-2022 (N=1241) and analyzed them using the Flesch Reading Ease (FRE) formula and a jargon calculator (the De-Jargonizer). In our analysis, we included the following study characteristics of each PLS: research topic, funding program, project size, length, publication year, and readability and jargon scores of the original funding proposal. Readability scores ranged from 1.1 to 70.8, with an average FRE score of 39.0 (95% CI 38.4-39.7). Moreover, 2.8% (35/1241) of the PLSs had an FRE score classified as \"plain English\" or better; none had readability scores in line with the average reading age of the UK population. Jargon scores ranged from 76.4 to 99.3, with an average score of 91.7 (95% CI 91.5-91.9) and 21.7% (269/1241) of the PLSs had a jargon score suitable for general comprehension. Variables such as research topic, funding program, and project size significantly influenced readability and jargon scores. The biggest differences related to the original proposals: proposals with a PLS in their application that were in the 20% most readable were almost 3 times more likely to have a more readable final PLS (incidence rate ratio 2.88, 95% CI 1.86-4.45). Those with the 20% least jargon in the original application were more than 10 times as likely to have low levels of jargon in the final PLS (incidence rate ratio 13.87, 95% CI 5.17-37.2). There was no observable trend over time. Most of the PLSs published in the NIHR's National Journals Library have poor readability due to their complexity and use of jargon. None were readable at a level in keeping with the average reading age of the UK population. There were significant variations in readability and jargon scores depending on the research topic, funding program, and other factors. Notably, the readability of the original funding proposal seemed to significantly impact the final report's readability. Ways of improving the accessibility of PLSs are needed, as is greater clarity over who and what they are for.

What is this summary about? This is a summary of the results from a clinical study of treatment for people with severe hemophilia A or moderately severe to severe hemophilia B without inhibitors. Because severe hemophilia A and B predominantly affect men and boys, this study only included men and boys aged 12 to 74 years. The study was published in Blood . People with hemophilia either have low amounts of clotting factors or are missing certain clotting factors in their blood. There are medicines that people with hemophilia can take to replace the missing clotting factor. These medicines must be injected into a vein and are usually given more than once a week. Marstacimab is an antibody that works by attaching to a protein in the blood called tissue factor pathway inhibitor (or TFPI). TFPI works separately from clotting factors. Marstacimab helps the balance between blood flowing freely and clotting. Marstacimab is given by a simple injection under the skin (not in a vein or muscle) and can be taken less often than clotting factors. Marstacimab may offer a new way to help people with hemophilia. What were the results of the study? The study showed that men with hemophilia had fewer treated bleed events during 1 year with marstacimab treatment compared to previous factor replacement therapy. The researchers reported that marstacimab was safe and most side effects were mild to moderate. What do the results mean? These studies showed that marstacimab could help prevent bleeding in men with hemophilia A or B, without inhibitors.

Although critical to enacting change, effectively communicating clinical and public health research results remains a challenge. In a webinar that occurred on December 7, 2023, a group of clinical and public health researchers and communications specialists convened to share their experiences using plain language materials to communicate research results. Herein, they provide practical guidance and case examples of lay summaries, infographics, data dashboards, and zines, along with challenges and potential solutions. Discussion illuminated the critical importance of partnering with communities who represent the intended beneficiaries of the research to plan, create, and disseminate materials. Accordingly, researchers should plan early, prepare, and dedicate resources for results dissemination. Researchers can use this guidance to develop plain language research dissemination materials, help connect with their audiences to inform and influence their understanding, and empower action to ultimately improve health and well-being.

Cochrane plain language summaries (PLSs) aim to make systematic review findings more accessible to the general public. However, inconsistencies in how conclusions are presented may impact comprehension and decision-making. Classifying PLSs based on conclusiveness can improve clarity and facilitate informed health decisions. This study aimed to develop and evaluate deep learning language models for the classification of PLSs according to 3 levels of conclusiveness (conclusive, inconclusive, and unclear) and to compare their performance with a general-purpose large language model (GPT-4o). We used a publicly available dataset containing 4405 Cochrane PLSs of systematic reviews published until 2019, already classified by humans according to 9 categories of conclusiveness regarding the intervention's effectiveness or safety. We merged these categories into 3 classes based on the strength of conclusiveness: conclusive, inconclusive, and unclear. For the fine-tuning, we used Scientific Bidirectional Encoder Representations from Transformers (SciBERT), a pretrained language model trained on 1.14 million papers primarily from the health sciences, and Longformer, a transformer model designed specifically to process long documents. The script was developed using the Python programming language and the PyTorch framework. We computed evaluation metrics using the scikit-learn machine learning library and determined the area under the curve of the receiver operating characteristic (AUCROC) to measure the model performance in balancing sensitivity and specificity. We also analyzed a separate set of 213 PLSs and compared the predictions of our pretrained models with both manual verification and outputs generated by ChatGPT. The model based on SciBERT achieved a balanced accuracy of 56.6%. The AUCROC was 0.91 for \"conclusive,\" 0.67 for \"inconclusive,\" and 0.75 for \"unclear\" conclusiveness classes. The Longformer-based model had a balanced accuracy of 60.9%, with AUCROCs of 0.86 for \"conclusive,\" 0.67 for \"inconclusive,\" and 0.72 for \"unclear\" conclusiveness classes. Both models underperformed compared with ChatGPT, which demonstrated higher accuracy (74.2%), better precision and recall, and a higher Cohen κ (0.57). Fine-tuning 2 transformer-based language models showed mixed results in classifying Cochrane PLSs by conclusiveness, likely due to semantic overlap and subtle linguistic differences. Despite satisfactory internal test metrics, the fine-tuned models failed to generalize to newly published PLSs, where performance dropped to near-chance levels. These findings suggest that general-purpose large language models like GPT-4o may currently offer more reliable results for practical classification tasks in biomedical applications.

Plain language summaries (PLSs) of Cochrane systematic reviews are a simple format for presenting medical information to the lay public. This is particularly important in oncology, where patients have a more active role in decision-making. However, current PLS formats often exceed the readability requirements for the general population. There is still a lack of cost-effective and more automated solutions to this problem. This study assessed whether a large language model (eg, ChatGPT) can improve the readability and linguistic characteristics of Cochrane PLSs about oncology interventions, without changing evidence synthesis conclusions. The dataset included 275 scientific abstracts and corresponding PLSs of Cochrane systematic reviews about oncology interventions. ChatGPT-4 was tasked to make each scientific abstract into a PLS using 3 prompts as follows: (1) rewrite this scientific abstract into a PLS to achieve a Simple Measure of Gobbledygook (SMOG) index of 6, (2) rewrite the PLS from prompt 1 so it is more emotional, and (3) rewrite this scientific abstract so it is easier to read and more appropriate for the lay audience. ChatGPT-generated PLSs were analyzed for word count, level of readability (SMOG index), and linguistic characteristics using Linguistic Inquiry and Word Count (LIWC) software and compared with the original PLSs. Two independent assessors reviewed the conclusiveness categories of ChatGPT-generated PLSs and compared them with original abstracts to evaluate consistency. The conclusion of each abstract about the efficacy and safety of the intervention was categorized as conclusive (positive/negative/equal), inconclusive, or unclear. Group comparisons were conducted using the Friedman nonparametric test. ChatGPT-generated PLSs using the first prompt (SMOG index 6) were the shortest and easiest to read, with a median SMOG score of 8.2 (95% CI 8-8.4), compared with the original PLSs (median SMOG score 13.1, 95% CI 12.9-13.4). These PLSs had a median word count of 240 (95% CI 232-248) compared with the original PLSs' median word count of 364 (95% CI 339-388). The second prompt (emotional tone) generated PLSs with a median SMOG score of 11.4 (95% CI 11.1-12), again lower than the original PLSs. PLSs produced with the third prompt (write simpler and easier) had a median SMOG score of 8.7 (95% CI 8.4-8.8). ChatGPT-generated PLSs across all prompts demonstrated reduced analytical tone and increased authenticity, clout, and emotional tone compared with the original PLSs. Importantly, the conclusiveness categorization of the original abstracts was unchanged in the ChatGPT-generated PLSs. ChatGPT can be a valuable tool in simplifying PLSs as medically related formats for lay audiences. More research is needed, including oversight mechanisms to ensure that the information is accurate, reliable, and culturally relevant for different audiences.

What is this summary about? People with overactive bladder need to use the bathroom many times a day to urinate (pee). This need may often be sudden and may cause some people with overactive bladder to have accidental bladder leakage. The EMPOWUR trial looked at how well a medicine called vibegron worked to help people with overactive bladder. The study also included another drug that was already available for treating overactive bladder called tolterodine and a pill with no medicine called a placebo. Both vibegron and tolterodine were compared with placebo. Participants had improvements in their overactive bladder symptoms after taking either vibegron or tolterodine compared to placebo. The medicine vibegron was approved in 2020 by the US Food and Drug Administration (also called the FDA) to treat overactive bladder. Researchers next wanted to see how well vibegron worked in people from the EMPOWUR trial split into 2 groups. One group was made of participants with overactive bladder who have accidental leakage. The second group was made of participants with overactive bladder who do not have accidental leakage. This is a plain language summary of the study of how well vibegron works for those 2 groups from the EMPOWUR study that was published in the International Journal of Clinical Practice. What were the results? Study participants who took vibegron needed to pee fewer times per day. The number of times they had little warning before the need to pee was also lower. The results were the same for study participants who did and did not have accidental leakage related to overactive bladder. What do the results mean? This study suggests that vibegron can improve symptoms in people with overactive bladder whether or not they have accidental bladder leakage.

Summary What is this summary about? • The Asthma Impairment and Risk Questionnaire (AIRQ®) has been designed and tested to measure patients’ levels of asthma control in a healthcare setting. • Unlike other available questionnaires that only assess asthma symptoms that can be bothersome or limit a person’s activities and quality of life (impairment-related symptoms), the AIRQ also includes questions related to risk of an asthma attack. This allows for a broader measurement of asthma control and a prediction of the chance of having future asthma attacks. • AIRQ scores are linked to a patient’s own experience of their health and how it impacts their daily life (health-related quality of life). • The AIRQ may make it easier for patients and healthcare professionals to have shared decision-making discussions that can lead to better asthma care and asthma outcomes. • This document summarizes several published studies of the AIRQ in people with asthma.