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Radiology reports are typically written in language that is difficult for patients to understand. Large language models (LLMs) excel at simplifying text. We aimed to evaluate the ability of LLMs to improve the understanding of radiology reports. In this systematic review and meta-analysis, we searched CENTRAL, MEDLINE, and Embase from inception to Nov 11, 2025, without restrictions on language. Full-text articles and preprints were considered for inclusion. Eligible studies applied LLMs to simplify radiology reports and had these reports assessed by members of the public or medical professionals. We excluded studies that focused solely on dialogues with interactive chatbots, preimaging leaflets, educational materials, appointment letters, or summarising findings without simplifying them for patients. Search results were screened independently by two authors and full-text review and data extraction were done by three authors; disagreements were resolved by consensus. The main outcomes were patient, public, and clinician evaluations (Likert scores) and text readability metrics. We assessed study quality with the MAIC-10 tool. This study was registered with PROSPERO (CRD420251027489). We identified 2385 records, of which 38 studies were eligible. These 38 studies generated 12 922 simplified reports, assessed by 508 evaluators (387 lay people and 121 clinicians). 35 (92%) of 38 studies used OpenAI GPT models and 29 (76%) produced simplified reports in English. Patients perceived LLM-rewritten reports as significantly more understandable than radiologist reports (mean Likert score 4·04 [SD 1·20] for simplified reports vs 2·16 [SD 0·94] for original reports; mean difference 2·00 [95% CI 1·54-2·46]). Clinicians rated LLM-rewritten reports highly for accuracy (mean 4·45 [95% CI 4·27-4·63]; 27 studies) and completeness (mean 4·53 [95% CI 4·30-4·76]; 14 studies). Readability was improved across imaging modalities, with lower Flesch-Kincaid Grade Level for LLM-rewritten reports, including a mean difference of -6·20 (95% CI -6·91 to -5·48) for CT, -5·07 (-5·99 to -4·15) for x-ray, and -5·0 (-6·0 to -4·0) for MRI. The error rate in LLM-rewritten reports was 7·2% (95% CI 5·1%-10·0%; 13 studies) and 0·9% (95% CI 0·6-1·5%; 2 studies) for clinically significant errors. LLM-simplified radiology reports improved patient-perceived understanding and readability and were rated by clinicians as largely accurate and complete, although a small proportion contained clinically significant errors. LLM-based simplification shows promise for making radiology communication more patient-centred, but further evaluation of its effect on patient outcomes and clinical workflows is required. National Institute for Health and Care Research Sheffield Biomedical Research Centre.

Mental health problems among university students are a growing global concern, yet limited counseling resources and inadequate understanding of counseling procedures often delay timely help-seeking. Informed consent forms (ICFs) are essential for safeguarding autonomy and clarifying counseling procedures, but many universities' counseling ICFs are incomplete, ambiguous, or overly technical. Large language models (LLMs) may offer scalable assistance for improving clarity and accessibility. This study aimed to evaluate whether LLM-based rewriting could improve the structure, readability, content quality, and comprehensibility of university counseling ICFs, and compared 2 advanced models (ChatGPT [GPT-5] and Grok-4). We conducted a comparative evaluation of counseling ICFs collected from 33 Chinese universities (original texts) and generated 2 rewritten versions for each ICF using ChatGPT (GPT-5) and Grok-4. A multidimensional framework assessed (1) textual structure and readability, (2) expert-rated content quality from a counselor perspective, and (3) volunteer-rated reading comprehension from a client perspective. Comparisons between original and rewritten texts were performed using Wilcoxon signed rank tests, with linear mixed-effects models used to validate results while accounting for rater variability. Compared with the originals, both LLM-rewritten ICFs showed significant improvements across all evaluated dimensions. The mean Lee-Yang Readability Index decreased from 28.68 (SD 5.69) to 22.39 (SD 2.13) with ChatGPT (GPT-5) and 24.37 (SD 2.32) with Grok-4 (both P<.001), and mean tone friendliness increased from 2.57 (SD 0.29) to 2.67 (SD 0.12) and 2.67 (SD 0.13), respectively. The mean expert-rated content quality improved from 45.33 (SD 8.74) to 52.54 (SD 7.92) and 55.49 (SD 7.81) (P<.001), driven mainly by higher completeness and specificity of key information. The mean volunteer-rated reading comprehension scores increased from 19.02 (SD 1.32) to 22.33 (SD 0.81) and 22.05 (SD 0.90) (P<.001), indicating improved clarity, readability, and acceptability. Across structural features, Grok-4 tended to produce longer rewritten forms than the originals, highlighting a potential trade-off between added informational content and document length. In this comparative evaluation of 33 Chinese university counseling ICFs, LLM-based rewriting was associated with improved readability, expert-rated content quality, and volunteer-rated comprehension relative to original forms. These findings suggest that LLMs can support the optimization of counseling documentation; however, implementation should consider practical constraints (eg, document length) and retain human oversight.

We reconsider information-theoretic principles, such as the maximum entropy principle/minimum Massieu potential principle, from the perspective of the dual probability distribution. This is introduced through Sanov's Lemma for the multinomial distribution. The dual correspondence becomes asymptotically manifest. The Massieu potential is rewritten as the Kullback-Leibler divergence between the dual probability distribution and the dual reference distribution. Similarly, the dual potential is rewritten as the cumulant generating function with respect to the dual reference distribution. This perspective gives us new insight into information-theoretic principles. As the dual probability distribution naturally arises in data sampling, we anticipate that this new perspective will play a significant role in data analysis.

With the widespread adoption of large language models such as ChatGPT, distinguishing AI-generated text from human-written content has become increasingly challenging. Existing detection methods often rely solely on semantic representations and exhibit limited robustness, particularly when texts are paraphrased or rewritten. This study proposes an integrated detection framework that combines contextual semantic embeddings with auxiliary surface-level features, including pronunciation-related textual cues and handcrafted statistical descriptors. Specifically, a RoBERTa encoder is employed to capture deep contextual semantics, while a convolutional neural network aggregates multi-scale representations. In parallel, a set of text-derived structural, lexical, and readability features-serving as proxies for phonetic and stylistic regularities-are incorporated to enrich the representation space. Rather than introducing a fundamentally new detection paradigm, the proposed approach emphasizes feature-level fusion and systematic empirical evaluation. Experiments on the HAGTC dataset and a ChatGPT-written abstract dataset show that the proposed RoBERTa-CNN framework consistently outperforms several strong baselines in terms of accuracy and F1 score. Notably, the model demonstrates improved robustness in detecting rewritten AI-generated texts. Ablation studies further confirm that integrating multiple feature types significantly enhances detection performance. These results indicate that combining contextual representations with auxiliary surface features offers a practical and effective direction for AI-generated text detection.

Data scarcity is a long-standing challenge in the vision-language navigation (VLN) field, which extremely hinders the generalization of agents to unseen environments. Previous works primarily rely on additional simulator data or web-collected images/videos to improve the generalization. However, the simulator environments still face limited diversity, and the web-collected data often require extensive labor to remove the noise. In this article, we propose a Rewriting-driven AugMentation (RAM) paradigm for VLN, which directly creates the unseen observation-instruction pairs via rewriting human-annotated training data. Benefiting from our rewriting mechanism, new observation-instruction pairs can be obtained in both simulator-free and labor-saving manners to promote generalization. Specifically, we first introduce object-enriched observation rewriting, where we combine vision-language models (VLMs) and large language models (LLMs) to derive rewritten object-enriched scene descriptions, enabling observation synthesis with diverse objects and spatial layouts via text-to-image generation models (T2IMs). Then, we propose observation-contrast instruction rewriting, which generates observation-aligned rewritten instructions by requiring LLMs to reason the difference between original and new observations. We further develop a mixing-then-focusing training strategy with a random observation cropping scheme, effectively enhancing data distribution diversity while suppressing augmentation data noise during training. Experiments on both the discrete environments (R2R, REVERIE, and R4R datasets) and continuous environments (R2R-CE dataset) show the superior performance and impressive generalization ability of our method.

Psychiatric discharge summaries are vital for ensuring continuity of care, yet they are often written in technical language that can be difficult for patients to understand and may cause emotional distress or reinforce stigma. With increasing patient access to medical records, there is a pressing need to develop communication tools that are both comprehensible and emotionally safe. This study aimed to evaluate the diagnostic fidelity, linguistic clarity, emotional sensitivity, treatment comprehension, and readability of psychiatric discharge summaries rewritten by ChatGPT-4 based on real clinical cases. This was the first study in South America to examine the use of a generative language model for rewriting psychiatric discharge summaries. A mixed-methods, observational cross-sectional design was applied. Twenty-five anonymized clinical cases were rewritten using ChatGPT-4. Three psychiatrists independently assessed each AI-generated summary across four dimensions: diagnostic fidelity, clarity of language, perceived emotional risk, and understanding of treatment. Readability was evaluated using the Fern&#xe1;ndez-Huerta Index and the INFLESZ Scale. A thematic analysis of evaluators' written comments was also conducted. Summaries generated by ChatGPT-4 were rated positively, particularly for clarity and treatment explanation. Significant improvements in readability were observed across all diagnostic groups (p&#x2009;<&#x2009;.001), with mean values surpassing recommended thresholds for general comprehension. However, five summaries remained below those thresholds, and some diagnostic inaccuracies were noted (e.g. omissions in bipolar disorder). Evaluators also highlighted emotionally charged or stigmatizing language in a few cases. ChatGPT-4 can enhance the accessibility and emotional appropriateness of psychiatric discharge communication, supporting more patient-centered care. Nevertheless, professional oversight remains critical to ensure clinical accuracy and contextual sensitivity. Future research should include patient feedback, assess long-term outcomes, and explore hybrid human-AI collaboration models.

Online patient educational materials (PEMs) have poor readability, limiting their intended purposes in improving patient comprehension of health topics. Orthopaedic oncology PEMs are particularly complex. Although ChatGPT has demonstrated limited success in simplifying PEMs to the recommended sixth-grade reading level, other large language models (LLMs) have not been thoroughly evaluated. The goals of this study were to (1) assess baseline readability of online orthopaedic oncology PEMs, (2) evaluate five LLMs (ChatGPT-4o, Google Gemini, DeepSeek AI, Microsoft Copilot, and Meta AI) for improving readability while preserving accuracy and comprehension, and (3) to examine tradeoffs when PEMs were simplified below the sixth-grade level. Seventy-two PEMs were collected from academic and professional sources. Readability metrics included the Flesch-Kincaid Grade Level (FKGL), Gunning Fog Index (GFI), and Flesch Reading Ease (FRE). Each PEM was rewritten by the five LLMs using the prompt: "rewrite this document to a sixth-grade reading level." Two independent graders then evaluated outputs for comprehension and accuracy (F1 score). ANOVA with pairwise comparisons assessed differences among LLMs and versus baseline (PEMs as written). A secondary analysis evaluated the effect on readability, accuracy, and comprehension of prompts to the fifth-grade, fourth-grade, and third-grade reading level. Baseline FKGL (8.7 &#xb1; 1.5) was between the eighth-grade and ninth-grade reading level, and GFI (10.5 &#xb1; 1.9) was slightly higher. Baseline FRE was 53.9 &#xb1; 8.2. All LLMs significantly improved readability (P < 0.001), and ChatGPT-4o, DeepSeek AI, and Google Gemini conversion produced the most readable outputs. Google Gemini achieved the highest F1 score of 0.986 (range: 0.765-0.986) and 100% comprehension. Accuracy and comprehension were compromised for MetaAI when prompted below sixth grade. ChatGPT-4o, Google Gemini, and DeepSeekAI effectively improved readability while preserving comprehension and accuracy. These findings may guide patient use of LLMs and inform healthcare-AI partnerships.

Medical visual question answering (Med-VQA) has emerged as a critical application of artificial intelligence within a short period of time. Large language models (LLMs) and vision-language models (VLMs) have fundamentally rewritten the architecture of medical question answering (QA). This study aims to systematically analyze recent developments in Med-VQA. Like past methods, which were simple, text-heavy database systems, there has been a shift toward multimodal frameworks. Recent methods are now highly capable of explaining radiology, pathology, and dermatological images along with clinical questions. This review was conducted following PRISMA guidelines, covering 27 representative studies published in various databases, using predefined inclusion and exclusion criteria. The findings reveal a clear shift toward generative models, supported by retrieval mechanisms and structured reasoning strategies such as Chain-of-Thought and multi-agent frameworks. Generative models, along with retrieval-augmented generation (RAG) and preference optimization, are not just more consistent than traditional classification-based methods but also can enable free-form clinical question answering. Though frameworks like multi-agent and hierarchical CoT have significantly improved interpretability and mitigated hallucinations, they also come with some limitations, like higher computational time, multi-view analysis, multi-lingual question answering, lack of standardized evaluation and exploration, domain-specific evaluation, and real-world clinical settings. Med-VQA systems demonstrate significant potential as a clinical decision answer generation with a vision language model. Future work should focus on computational efficiency during real-world validation, fairness evaluation, standardized diagnostic benchmarks, and interpretable reasoning frameworks including specialized domain knowledge and practical skills.

In photosystem II (PSII) chlorophyll (Chl) fluorescence yield (F) rises during low-to-high light induction. Based on a critical review of the literature, the following evidence is summarized. (1) As the primary acceptor quinone QA gets reduced, fluorescence immediately rises to Fc =1.8 Fo. (2) During microseconds of illumination following QA reduction, Fc rises to Ff where excitation is terminated via carotenoid triplet states (3Car). (3) After many milliseconds of illumination, as the secondary acceptor quinone QB is reduced, fluorescence rises to a maximum Fm, wherein excitation is still terminated via 3Car. This dual phase fluorescence rise is driven by protein conformation changes. The two phases suggest that the C2S2M2 structure of the PSII dimer (Caffarri et al. 2009) may be rewritten as 2SCM such that for a monomer the arrangement is S - CP43 - D1D2 - CP47 - M. The D1- and D2- antenna branches are excitonically separated. Fluorescence of the D1 pigment-protein branch rises during microseconds after QA reduction, that of the D2 branch rises on a time scale of milliseconds during QB reduction. A hypothesis is proposed here based on electrostatic profiling of photosynthetic pigments (Sirohiwal et al. 2021; Saito et al. 2023). When acceptor quinones are oxidized excitation flows through the stromal Chl layer of Chl-proteins CP43 and CP47 to the photochemical Pheophytin/Chl based reaction center. As QA gets reduced during microseconds in CP43, and QB gets reduced during milliseconds in CP47, an associated dynamic signal induces protein conformation changes shifting excitations from the stromal to the lumenal Chl layer. With this, excitation becomes diverted from the photosynthetic reaction center and connected to the characteristically lumenal ChlZD1 and ChlZD2. Together with their nearby CarD1 and CarD2 these ChlsZ form quenching centers, terminating excitation via 3Car with a lifetime of about 1 ns, sufficient to induce the maximum Fm fluorescence.

Effective communication about clinical trials is essential, as low enrollment undermines scientific validity and contributes to health care inequities. However, recruitment remains a persistent challenge, particularly among older adults, minority populations, and individuals with limited health literacy. Although large language models (LLMs) show promise in understanding and generating health information, it is unclear whether these generative artificial intelligence (AI) tools can improve the content of hospitals' frequently asked questions (FAQ) pages to enhance public attitudes and intentions toward clinical trial participation. This study aimed to compare clinical trial FAQ from health organizations and hospitals with versions rewritten by LLMs to examine whether the generated content improves public attitudes and intentions toward clinical trial participation and to identify the mechanisms underlying these effects. A total of 308 question-answer pairs were collected from the FAQ pages of 38 health organizations and hospitals, categorizing them into 52 types and selecting the 11 most frequent for testing. A comparative survey experiment was conducted with 440 participants randomly assigned to one of the two survey stimuli: the original FAQ versus the GPT-4o-generated answers emphasizing comprehension and empathy. The study compared the impact of AI-generated versus standard FAQ content on attitudes toward clinical trials and examined Theory of Planned Behavior constructs to determine for whom and how AI information is most effective. Participants were recruited through CloudResearch, yielding a 96.94% completion rate, resulting in 440 valid responses across the 2 types of content exposure. Participants who viewed GPT-4o-generated information (mean 0.26, SD 0.65) showed a marginally greater positive change in outcome evaluation attitudes than those who viewed standard FAQ (mean 0.13, SD 0.70; P=.05; 95% CI 0.00-0.25). Follow-up linear regression analyses revealed that several individual factors significantly moderated the effect of the information type (FAQ vs GPT-4o) on attitude change, including age (mean difference 0.87, SE 0.33; 2-tailed t394=2.62; P=.009); race (mean difference 0.36, SE 0.15; t383=2.47; P=.01); risk aversion (B=0.12; SE 0.06; t383=2.23; P=.03); fear of ineffective treatment (B=0.11; SE 0.05; t383=2.03; P=.04); and fear of unknown treatment effects (B=0.21; SE 0.07; t383=3.10; P=.002). This study is the first to apply the Theory of Planned Behavior to compare LLM-rewritten versus original FAQ content for clinical trial communication. The findings show that the GPT-4o-generated responses improved attitudes among traditionally underrepresented groups, including older adults, Black participants, and those with higher uncertainty avoidance or treatment concerns. These attitude gains were positively linked to participation intentions, suggesting that AI-generated language can enhance public attitudes, perceptions, and engagement with clinical research.

To evaluate the readability, understandability, and actionability of current orthopaedic discharge instructions and assess the effectiveness of generative artificial intelligence (AI) in addressing these areas of improvement. Orthopaedic discharge instruction documents were collected from a large academic hospital from March to May 2025. Subcategories frequently associated with postoperative inquiries-activities, complications and follow-up, medications, narcotics, pain management, physical therapy, and wound care-were extracted for analysis. ChatGPT-4 was used to generate improved discharge instructions using structured prompting strategies. Readability and text complexity measures were assessed using 5 validated readability tests whereas understandability and actionability were assessed using a modified Patient Education Materials Assessment Tool (PEMAT). Paired t-tests were used to compare scores between original and ChatGPT-rewritten content whereas comparisons across subcategories were achieved with independent samples t-tests (P&#x2009;<&#x2009;.05). Fifty-two subcategory excerpts were identified across twelve distinct discharge instructions. ChatGPT significantly reduced the average reading grade level of discharge instructions from 9.8 to 7.5 across all content categories (P&#x2009;<&#x2009;.001). The wound care subcategory showed the lowest reading grade level before and after ChatGPT. The narcotics subcategory showed the highest reading grade level before ChatGPT, whereas the medications and physical therapy subcategories showed the highest reading grade level after ChatGPT. Understandability and actionability scores improved from 63.6% to 87.9% (P&#x2009;<&#x2009;.001) and from 44.4% to 73.6%, respectively (P&#x2009;<&#x2009;.001). Generative AI significantly improves the readability, understandability, and actionability of orthopaedic discharge instructions; however, not all documents achieved the desired sixth-grade reading level. Subcategory analysis revealed opportunities for targeted interventions to improve patient education in specific topics, such as narcotics, medication, or physical therapy-related instructions. This study describes a cost-effective, scalable AI intervention for improving orthopaedic discharge instructions which may improve clinical outcomes, reduce healthcare burden, and support patients' management of postoperative recovery.

The transition to university is a critical life stage characterized by increased autonomy, identity exploration, and new social and environmental influences. During this period, university students often exhibit low adherence to dietary guidelines. Among the determinants influencing healthy eating, cooking self-efficacy, the central construct of Social Cognitive Theory (SCT), is consistently associated with improved diet quality and is a frequent target of health interventions. However, no validated instrument exists to assess this construct among university students in Spain. Therefore, the goal was to develop and provide preliminary evidence of the Spanish Cooking Self-Efficacy Questionnaire (SCSEQ), a concise SCT-based instrument tailored to Mediterranean university settings. A 32-item questionnaire was developed through a review of existing instruments assessing cooking self-efficacy. Face validity was evaluated with Spanish food and nutrition experts (n&#x2009;=&#x2009;12) to assess the clarity and pertinence of the initial items. The revised Spanish Cooking Self-Efficacy Questionnaire (SCSEQ) was then pilot-tested with Spanish university students (n&#x2009;=&#x2009;73) from four Catalan universities. Exploratory factor analysis (EFA) was conducted to identify the underlying factor structure and detect problematic items. Internal consistency reliability was assessed using McDonald's &#x3c9;, and test-retest reliability over a two-week interval was evaluated using Pearson correlations. Face validity indicated overall clarity and adequacy. Four items were excluded and recombined, two items were added, and nine items were rewritten based on experts' feedback. After pilot testing, the questionnaire overall demonstrated high internal consistency (&#x3c9;&#x2009;=&#x2009;0.9). Items were reviewed based on factor loadings, item redundancy, theoretical relevance, and their contribution to scale-level internal consistency. EFA suggested a two-factor structure with good internal consistency (&#x3c9;&#x2009;=&#x2009;0.88 and &#x3c9;&#x2009;=&#x2009;0.82) and test-retest reliability (ICC&#x2009;=&#x2009;0.91, 95% CI [0.80, 0.96]). Three items with weak loadings were excluded. The final version consisted of 25 items and 2 subscales. The SCSEQ showed favorable preliminary psychometric properties.

For B-cell acute lymphoblastic leukemia (B-ALL), the dominance of traditional chemotherapy is being rewritten, with multiple promising novel treatment approaches continually emerging. Immunotherapy (blinatumomab, Blina or inotuzumab ozogamicin, InO) has demonstrated efficacy in the induction phase for elderly patients newly diagnosed with B-ALL, but its role in newly diagnosed patients fit for intensive chemotherapy is being explored. This study aims to evaluate the efficacy and safety of immunotherapy with or without low-intensity chemotherapy in fit patients newly diagnosed with B-ALL. We retrospectively enrolled 133 patients with newly diagnosed B-ALL who met the inclusion criteria at Qilu Hospital of Shandong University. Patients were divided into two groups based on whether Blina/InO was used in the first-line induction therapy regimen. The immunotherapy group (IG) consisted of 25 patients, while the chemotherapy group (CG) consisted of 108 patients. Propensity score matching (PSM) was performed to match the IG and CG patients in a 1:1 ratio based on age, sex, risk stratification, comorbidities and Philadelphia chromosome status. After matching, there were 25 patients in each group. Primary research objectives included remission rates and minimal residual disease (MRD) negativity rate. Secondary objectives included adverse events. Data management and statistical analysis were performed using SPSS and R software. The IG achieved a higher MRD negativity rate (88% vs. 48%, p&#x202f;=&#x202f;0.002). Compared with the CG, the IG had a higher median minimum of neutrophil count (p&#x202f;<&#x202f;0.001) and a higher median minimum of platelet count (p&#x202f;=&#x202f;0.001). Furthermore, the duration of neutrophil count < 0.5&#x202f;&#xd7;&#x202f;109/L (p&#x202f;=&#x202f;0.033), hemoglobin level < 80&#x202f;g/L (p&#x202f;=&#x202f;0.038) and platelet count < 30&#x202f;&#xd7;&#x202f;109/L (p&#x202f;=&#x202f;0.006) was shorter in the IG. They also experienced fewer pulmonary infections (44% vs. 84%, p&#x202f;=&#x202f;0.003). Additionally, the IG required fewer red blood cell transfusions (4 vs. 8 u, p&#x202f;=&#x202f;0.012) and platelet transfusions (0 vs. 48 u, p&#x202f;<&#x202f;0.001). In the PSM-based retrospective cohort study, an immunotherapy-based first-line treatment strategy showed promising early treatment responses and tolerability to hematologic toxicity in fit patients newly diagnosed with B-ALL. These exploratory findings provide additional real-world evidence.

Thousands of network nodes in the Internet of Things produce vast amounts of long-term time series. Predicting network traffic helps identify security risks and improve network management. In the past few years, Transformer-based models (Transformers) achieve superior predicting accuracy. However, the attention mechanism faces the challenge of balancing the expressivity and computational efficiency. Recently, an effective state space model named Mamba has been proposed. It demonstrates exceptional capabilities for modeling long-term dependencies. Meanwhile, its gateing network structure also provides inspiration for enhancing the attention mechanism. In this paper, we theoretically prove that the linear attention with rotary positional embeddings can be rewritten to the form similar to Mamba. Building on this insight, we design a scalable rotary position embedding (SRoPE) mechanism that introduces a scaling factor to adjust information flow while retaining the relative positional relationships. This confers a forget-gate-like capability on the model and allows seamless integration with existing multi-head mechanisms, achieving greater expressiveness than previous attention variants. We then propose Rotary Gated linear Attention (RoGAtten) for multivariate time series forecasting. RoGAtten is employed to capture inter-series dependencies. The SRoPE can provide series-wise discriminative identifier and adjust the strength of interactions between variables, enabling predictions that better align with domain knowledge. Extensive experiments on 8 real-world datasets show that RoGAtten reduces MSE by 3.85% and MAE by 1.71% compared to the state-of-the-art methods.

Pharmaceutical care in public health services is often hindered by the need for pharmacists to balance clinical, technical, and managerial tasks, limiting their ability to focus on patient care. The high prevalence of systemic arterial hypertension (SAH) combined with increased demand for healthcare and limited resources, further strains these services, making it difficult to meet the needs of the entire population. To address this, tools that help identify high-priority cases are essential for effective healthcare delivery. This study aimed to develop and validate a screening tool to identify patients with SAH who require pharmacotherapy follow-up in Primary Health Care settings. The study followed a four-step validation process: evaluation by experts, semantic analysis, application of the tool to SAH patients, and score determination. The initial instrument contained 36 items across four categories: Lifestyle, Comorbidities, Use of Health Services, and Use of Medications. Fleiss' Kappa coefficient of 0.66 indicated good agreement among the evaluators. After semantic analysis, ten items were rewritten, and additional information was included in eight items for clarity. The final version of the tool, applied to 828 patients, identified the most relevant items through composite reliability (rho) analysis. The validated instrument, ISPHAF-APS, contains 14 items across three dimensions: Comorbidities, Use of Health Services, and Use of Medications. A score of three or less indicates low priority, while a score above five suggests a higher need for pharmacotherapy follow-up. The ISPHAF-APS demonstrated adequate psychometric properties and can effectively identify SAH patients who would benefit most from pharmaceutical care.

Patient Information leaflets (PILs) from the British Association of Urologists (BAUS) are commonly used to communicate to patients about surgical procedures, but previous studies have highlighted that they are too difficult for some patients to read. BAUS PILs have since been rewritten using guidelines which emphasize readability. To identify if the readability of BAUS PILs has changed compared to historical versions. Current BAUS PILs (published 2020-2025) were compared with historical PILs with similar titles from 2014 to 2016 using a custom Python script. Readability scores improved by a significant (P&#x2009;<&#x2009;.001) albeit small amount (FKGL 8.83 vs 8.67; SMOG 11.86 vs 11.71; FRE 57.06 vs 57.47), and continue to have a suboptimal readability. The revised PILs had significantly fewer long sentences (10.11% vs 6.84% P&#x2009;<&#x2009;.0001), fewer sentences that used the "passive voice" (30.50% vs 16.22% P&#x2009;<&#x2009;.0001) and were shorter (1875.5 words vs 1726 P&#x2009;=&#x2009;.004). While there have been small improvements in urology PILs for patients, they remain too difficult for many patients, and more work is needed to improve readability.

ObjectivesThis study aimed to assess the readability of online information about semaglutide while also assessing understandability and quality.MethodsOzempic, Wegovy, and 'semaglutide' were individually searched. The non-sponsored results on the first five pages for each search were screened. The text from the included links were evaluated by two researchers for readability using SMOG and Flesch Reading Ease (FRE), for understandability using Patient Education Materials Assessment Tool (PEMAT) and for quality using DISCERN. A statistician ran reports for medians, interquartile ranges, and frequency statistics.Results61 links met evaluation criteria. Median scores for SMOG and FRE were 13th grade level and College. Fewer than 10% were at or below the recommended reading grade level. The median score of PEMAT was 62%. The median overall score of DISCERN was 4 out of 5.ConclusionsMost education available online about semaglutide medications is not written at the recommended reading level. Patient education on semaglutide needs to be rewritten to be at the recommended 8th grade reading level.