Large language models (LLMs) are increasingly being explored for patient education, but evidence from patient-facing clinical studies involving real patients remains limited. We conducted a mixed-methods systematic review of LLM-supported patient education in clinical settings, focusing on patterns of use, patient perspectives, and patient-reported outcomes. Searches of PubMed, Embase, Scopus, Web of Science, and CINAHL identified 48 eligible studies from 17 countries. LLMs were mainly used to answer patient questions, generate educational materials, and simplify medical information. Patients generally valued LLM-supported education for clarity, accessibility, and usefulness, but raised concerns about accuracy, privacy, personalization, trust, and emotional support. Quantitative findings were most consistent for cognitive and information-related outcomes, whereas evidence for satisfaction, trust, empathy, preference, behavioral outcomes, and longer-term effects was mixed or limited. Current evidence supports the use of LLMs as adjuncts to patient education, particularly for improving access to and understanding of medical information, while clinician–patient communication remains essential for personalized explanation, trust building, and shared decision-making.

Background:Large language models (LLMs) have attracted increasing attention in medical research and clinical practice and have been applied to processes related to evidence-based medicine (EBM). However, the extent of their integration with evidence-based Chinese medicine (CM) remains unclear. Methods:We systematically searched PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), and Wanfang Data from 30 November 2022 to 31 January 2026, with supplementary searches conducted in Google Scholar. Studies were included if they applied LLMs to EBM processes within a CM context or investigated LLMs in CM using established evidence-based research designs. Descriptive analysis summarized study characteristics, and findings were mapped according to the evidence ecosystem framework. Results:A total of 12 studies published between 2023 and 2025 were included. Most studies integrated LLMs into different stages of the EBM workflow within a CM context. At the evidence generation stage, studies explored the role of LLMs in identifying research priorities. At the evidence synthesis stage, LLM performance was evaluated in literature screening, data extraction, and risk-of-bias assessment. At the evidence translation stage, studies evaluated the performance of LLMs in guideline-related question answering and recommendation generation. At the evidence implementation stage, LLMs combined with knowledge graphs or retrieval-augmented generation were used to develop intelligent question-answering systems based on CM guidelines or standards. Conclusion:Existing studies suggest that LLMs have begun to be explored across multiple stages of evidence-based CM research and show potential for improving evidence synthesis efficiency and supporting knowledge translation and application. Protocol registration:Open Science Framework (https://osf.io/ztbd5/overview).

OBJECTIVES:The application of large language models (LLMs) to systematic review tasks is rapidly expanding, yet the transparency and methodological rigor of these evaluations remain unclear. We aimed to assess reporting transparency, methodological quality, and how authors frame claims and caveats in studies applying LLMs to systematic review tasks. STUDY DESIGN AND SETTING:We conducted a cross-sectional meta-epidemiological study by searching PubMed, Embase, Web of Science Core Collection, IEEE Xplore, and five other databases from inception to December 1, 2025, for peer-reviewed articles and preprints. We included empirical studies evaluating generative transformer-based LLMs (eg, Gemini) for core systematic review tasks (eg, screening, data extraction) against a reference standard. We assessed reporting transparency using an adapted Chatbot Assessment Reporting Tool and methodological quality using an adapted Quality Assessment of Diagnostic Accuracy Studies 2 tool. We also analyzed the frequency and strength of claims and caveats mentioned by the authors. The study is registered with the Open Science Framework (https://osf.io/8edhb). RESULTS:We identified and included 229 studies comprising 440 empirical tasks. Reporting transparency was moderate, with a mean item score of 0.52 (standard deviation 0.30) on a 0-1 scale, where higher values indicate more complete reporting. We observed substantial gaps in reproducibility-essential domains, including protocol information (mean score 0.12) and model details (0.30). Although 60.6% of assessments were rated as having a low risk, key safeguards against overfitting and data leakage were rarely reported; for example, locking the test set before prompt optimization, a basic protection against information leakage, was not reported in 99.8% of tasks. We identified 837 claims and 693 caveats. Authors framed claims weakly more often than strongly (66.8% vs 33.2%). Performance superiority over a comparator was the most common claim (64.8% of tasks). Readiness for practical use was claimed in 47.5% of tasks, almost always in qualified terms (93.3%). CONCLUSION:Studies applying LLMs to systematic review tasks are reported with moderate transparency but often omit reproducibility-critical details necessary to assess leakage and overfitting. Although authors frequently make claims about performance and practice readiness, these are typically expressed cautiously. Improved reporting standards and clearer safeguards are urgently needed before routine use of LLMs in evidence synthesis can be recommended. PLAIN LANGUAGE SUMMARY:LLMs, such as ChatGPT, are increasingly used to help carry out parts of systematic reviews, which summarize evidence to inform healthcare decisions. We examined 229 studies that tested LLMs on tasks such as screening articles, extracting data, and assessing study quality, covering 440 evaluations in total. On average, these studies reported their methods with moderate clarity, but often omitted information needed to repeat the work or judge whether the results were trustworthy. Notably, almost none described safeguards to ensure that the test data had not already influenced how the model was set up, a key step for avoiding overly optimistic results. Authors frequently described LLMs as performing well and nearly ready for practical use, though usually in cautious terms. Clearer reporting standards and stronger safeguards are needed before LLMs can be routinely relied upon in evidence synthesis.

Background:Annual influenza vaccination is recommended for solid organ transplant (SOT) recipients, with various strategies explored to enhance efficacy. Given the ongoing uncertainty about the benefits and potential risks of different strategies in influenza vaccination in SOT recipients, comparing the indirect comparisons of the available evidence is necessary. Methods:We searched MEDLINE, Embase, CENTRAL, and CINAHL from inception to March 2025 to identify randomized controlled trials that compared different influenza vaccinations in SOT recipients. We conducted a network meta-analysis and used the GRADE approach to assess the certainty of evidence, categorize interventions, and present the findings. The protocol for this systematic review was registered in PROSPERO (CRD42024537277). Results:A total of 13 articles met our eligibility criteria (with 2,298 participants) and assessed 10 vaccination strategies. The high-dose (60IMSD) influenza vaccine was superior to the standard influenza vaccination regarding the seroconversion rate for H1N1 (RR 2.27, 95% CI 1.52 to 3.39; high certainty) and H3N2 (RR 1.61, 95% CI 1.34 to 1.94; high certainty). All of the included vaccination strategies might have no difference in graft rejection and other adverse events compared with the standard vaccination. Conclusions:We found that the 60IMSD vaccine demonstrated better immunogenicity outcomes compared with all other influenza vaccines studied in SOT recipients. Additionally, we observed little or no difference among various influenza vaccination strategies in terms of graft rejection and other serious adverse events (SAEs) or non-serious adverse events (non-SAEs).

Evidence-based medicine (EBM), formalized in the 1990s, has redefined clinical practice by advocating the integration of research evidence, clinical expertise, and patient values. This paradigm has introduced methodological rigor through randomized controlled trials (RCTs) to establish causality, systematic reviews to synthesize findings, and the GRADE approach to evaluate evidence based on risk of bias, inconsistency, indirectness, imprecision, and publication bias. These advancements have shaped clinical guidelines, reduced practice variability, and influenced medical education toward evidence-based inquiry. Despite its contributions, EBM faces challenges in the evolving landscape of modern medicine. The lengthy process of evidence generation, often requiring years for trials and guideline updates, limits responsiveness to emerging health needs, as observed during the COVID-19 pandemic. The external validity of RCT results is constrained by strict inclusion criteria, posing difficulties in applying findings to diverse patient populations with comorbidities. Additionally, the siloed nature of evidence complicates comprehensive care for multifactorial conditions, while the annual influx of over one million medical publications overwhelms traditional synthesis methods. Artificial intelligence (AI) presents a promising avenue to address these issues, leveraging capabilities in processing heterogeneous data. Natural language processing may enhance literature analysis, machine learning could identify patterns in complex datasets, and causal inference might improve the reliability of observational data insights. These technologies hold potential to accelerate evidence development and tailor it to individual needs. This paper proposes digital intelligent evidence-based medicine (i-EBM) as a conceptual evolution of EBM, designed for the AI era. i-EBM envisions a three-layered framework. The data foundation layer aims to integrate structured evidence from RCTs, domain knowledge such as biomedical ontologies and traditional Chinese medicine principles, and multi- modal patient data, including electronic health records, genomics, and wearable device outputs. Knowledge graphs are proposed to link these elements into a unified, computable knowledge network. The intelligent processing layer seeks to apply AI for evidence retrieval, data extraction, quality assessment, and synthesis, potentially using large language models to assist these processes. The knowledge service layer intends to provide dynamic guidelines and individualized predictions, supported by ongoing human-machine collaboration to ensure clinical relevance and ethical considerations. i-EBM has the potential to mitigate EBM's limitations by facilitating real-time evidence updates, reducing knowledge fragmentation through integrated data, and offering personalized decision support. For instance, it may support precision medicine by connecting diverse data sources, with applications possibly extending to fields like oncology or traditional Chinese medicine. Future research could explore autonomous AI systems, optimized clinical workflows, and governance frameworks to address data privacy, bias, and global standardization. In conclusion, i-EBM offers a theoretical framework to extend EBM principles, harnessing AI's potential alongside human expertise to advance medical research and practice. Meanwhile, for issues such as the quantitative study of the complex intervention characteristics and syndrome differentiation patterns of traditional Chinese medicine, i-EBM can provide methodological support in data integration, pattern recognition, and causal inference, offering potential tools and insights for uncovering the intrinsic regularities of TCM evidence and optimizing its evaluative framework.

Network meta-analysis (NMA) plays an important role in comparative effectiveness research, particularly in fields such as traditional and complementary medicine, where multiple interventions often need to be assessed within a single evidence framework. However, conducting NMA remains labor-intensive, methodologically demanding, and difficult to complete efficiently using conventional workflows. Although recent advances in large language models have created new opportunities for supporting evidence synthesis, their routine use in NMA is still constrained by limited transparency, prompt dependency, and difficulty integrating with structured analytical procedures. In this study, we introduce SmartEBM, a web-based human-AI collaborative platform designed to support the full workflow of NMA. SmartEBM provides an integrated environment for title and abstract screening, full-text screening, data extraction, risk of bias assessment, statistical analysis, and certainty of evidence assessment. The platform is organized around a human-in-the-loop model, in which AI-assisted functions support repeated and labor-intensive tasks while researchers retain oversight of methodological judgement, verification, and final decision-making. Through its six functional modules, SmartEBM offers low-code interfaces, structured outputs, and verification-oriented workspaces that help connect major steps of evidence synthesis within one platform. Rather than functioning as a stand-alone automation tool, SmartEBM is intended as a practical platform for end-to-end NMA support. This platform-oriented approach may help make evidence synthesis more manageable, traceable, and accessible in routine research practice, especially in complex review settings.

Background: Integrated Chinese-Western medicine (ICWM) is a distinctive medical system that plays an important role in healthcare and has received increasing attention in recent years. To facilitate the dissemination of evidence in ICWM, we developed an Artificial Intelligence (AI)-empowered Clinical Evidence for Integrated Chinese-Western Medicine (ACE-iMed) platform. Methods: A multidisciplinary working group was established, including individuals with professional backgrounds in evidence-based medicine methodology, Chinese medicine (CM), Western medicine (WM), and ICWM clinical practice and research, and computer science. Through multiple rounds of discussions, the working group defined the framework and methodology of the platform, and then applied the platform to summarize evidence for eight diseases. Results: The ACE-iMed platform (website: www.aceimed.org) contains two interfaces. The first enables the developers to store and screen the literature, perform methodological quality assessments, and generate evidence summaries. The AI-empowered workflows showed good consistency and stability across multiple stages, including literature screening and assessment of risk of bias/methodological quality, and effectively support summarizing evidence for eight diseases. The second interface, intended for end users, provides synchronized access to the included literature and the generated summaries, enabling quick access to clinical question-oriented evidence resources. Conclusion: This study introduces an AI-empowered, clinical question-oriented ICWM evidence platform. Application across eight diseases demonstrated the platform's feasibility and practical utility. The platform not only supports the developers in summarizing evidence but also provides end users with a potential pathway to access evidence and its summaries.

Hypertension is a major risk factor for cardiovascular disease. Salt substitutes may reduce sodium intake while maintaining palatability, but comparative effects across formulations remain uncertain. We conducted a systematic review and frequentist random-effects network meta-analysis of randomised controlled trials in adults comparing salt substitutes with regular salt, other substitutes or no intervention. Databases (PubMed, Embase, CENTRAL, CNKI, Wanfang), WHO-ICTRP and ClinicalTrials.gov were searched from inception to Oct 3, 2025 (PROSPERO CRD42023451859). We assessed the risk of bias using a modified Cochrane tool and conducted a random-effects network meta-analysis, with evidence certainty evaluated through the GRADE approach. We included 34 randomised controlled trials involving 37,063 participants across 15 countries (17 from China, 17 from other countries; mean age 62.3 years). Our results indicate that moderate-potassium and low-sodium salt substitutes (25–40

Background:Molecular point-of-care testing (mPOCT) offers rapid identification of respiratory pathogens, but its impact on antibiotic use and patient outcomes remains uncertain. We aimed to comprehensively evaluate the effects of mPOCT on antibiotic use and major clinical outcomes in patients presenting with acute respiratory tract infections (ARTIs). Methods:We searched MEDLINE, Embase, Web of Science, CENTRAL, CNKI, and Wanfang Data from inception to July 1, 2025, for randomised controlled trials (RCTs) evaluating mPOCT for patients presenting with ARTIs (PROSPERO CRD420251069333). The primary outcome was antibiotic use, assessed using pooled risk ratio (RR) with random-effects models. Risk of bias and certainty of evidence were assessed using the Risk Of Bias instrument for Use in SysTematic reviews-for Randomised Controlled Trials (ROBUST-RCT) and core Grading of Recommendations, Assessment, Development and Evaluation (GRADE), respectively. Findings:We included 25 RCTs involving 12,638 patients, of whom 61.0% were adults. Overall, mPOCT probably had little to no important effect on antibiotic use (RR 0.95, 95% CI 0.90-1.00; moderate certainty) or treatment duration (mean difference -0.44 days, 95% CI -0.98 to 0.09; moderate certainty). In adults, high-certainty evidence showed no effect on antibiotic use (RR 1.00, 95% CI 0.98-1.02), whereas in children, low-certainty evidence suggested a potential reduction (RR 0.79, 95% CI 0.65-0.97). Although mPOCT increased appropriate antibiotic prescribing (RR 2.07, 95% CI 1.55-2.77; moderate certainty), it did not affect 30-day mortality (RR 0.97, 95% CI 0.82-1.15; high certainty) and intensive care unit admission (RR 0.90, 95% CI 0.65-1.25; high certainty). Interpretation:Moderate to high certainty evidence suggests that mPOCT does not meaningfully reduce overall antibiotic use or improve patient outcomes, particularly in adults, despite enhancing prescribing appropriateness. Routine use of mPOCT for adults with ARTIs is therefore not supported. Funding:National Natural Science Foundation of China, the Postdoctoral Science Foundation, the Chongqing Municipality Joint Science and Health Major Medical Research Project, Outstanding Youth in Science and Technology, the Chongqing Youth Talent Fund, and the Research Foundation Flanders.

ETHNOPHARMACOLOGICAL RELEVANCE:Traditional herbal medicine has been used for centuries to treat respiratory infections, with documented historical applications for conditions characterized by persistent fever, productive cough, and lung inflammation. These traditional preparations represent indigenous medical knowledge that may offer alternative approaches to modern antibiotic-resistant pulmonary infections, particularly given the global crisis of antimicrobial resistance and diminishing treatment options. AIM OF THE STUDY:To systematically evaluate the clinical efficacy and pharmacodynamic basis of traditional herbal medicine in treating multidrug-resistant pulmonary infections through bi-directional validation of clinical and laboratory evidence. RESULTS:From 16,896 records, we identified 100 studies examining 42 traditional preparations against multidrug-resistant pulmonary pathogens. However, evidence quality was highly unbalanced: only Tanreqing injection possessed sufficient data for meta-analysis, demonstrating modest but significant improvements in clinical efficacy (RR = 1.20, 95 %CI: 1.12-1.28) and bacterial clearance (RR = 1.29, 95 %CI: 1.12-1.49) based on 596 patients. Most other preparations relied on small, poorly controlled studies. Bi-directional validation revealed that merely nine preparations had both clinical and pharmacodynamic evidence. Laboratory studies consistently demonstrated synergistic effects with antibiotics, with some combinations achieving substantial MIC reductions (up to 87.52 %). Animal studies confirmed anti-inflammatory properties and improved survival rates, though translation to clinical settings remains uncertain. CONCLUSIONS:Traditional herbal medicine shows potential as complementary therapy for multidrug-resistant pulmonary infections through multi-target therapeutic actions and antibiotic synergy. However, evidence quality remains heterogeneous, with most preparations lacking sufficient high-quality clinical data. Future research should prioritize larger randomized trials, standardized microbiological reporting, and mechanistic investigation to optimize clinical application.

This quality improvement study examines the efficacy of a large language model to evaluate guidelines for therapeutic drug monitoring compared with human apparaisers.

The advancement of large language models (LLMs) presents promising opportunities to enhance evidence synthesis efficiency, particularly in data extraction processes, yet existing prompts for data extraction remain limited, focusing primarily on commonly used items without accommodating diverse extraction needs. This research letter developed structured prompts for LLMs and evaluated their feasibility in extracting data from randomized controlled trials (RCTs). Using Claude (Claude-2) as the platform, we designed comprehensive structured prompts comprising 58 items across six Cochrane Handbook domains and tested them on 10 randomly selected RCTs from published Cochrane reviews. The results demonstrated high accuracy with an overall correct rate of 94.77% (95% CI: 93.66% to 95.73%), with domain-specific performance ranging from 77.97% to 100%. The extraction process proved efficient, requiring only 88 seconds per RCT. These findings substantiate the feasibility and potential value of LLMs in evidence synthesis when guided by structured prompts, marking a significant advancement in systematic review methodology.

ObjectiveThe prevalence of long COVID among cancer patients remains unknown. This study aimed to determine the prevalence of long COVID and explore potential risk factors among cancer patients.MethodsA systematic search was performed on PubMed, Web of Science, and Embase from database inception until 21 March 2024, to identify studies that reported long COVID in cancer patients. Two investigators independently screened the studies and extracted all information about long COVID in cancer patients for subsequent analysis. Methodological quality was assessed using the “Joannagen Briggs Institute (JBI) Critical Appraisal Checklist for Studies Reporting Prevalence Data”.ResultsA total of 13 studies involving 6,653 patients were included. The pooled prevalence of long COVID was 23.52% [95% confidence interval (CI), 12.14% to 40.64%] among cancer patients reported experiencing long COVID after acute severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The pooled prevalence of any long COVID in cancer patients was 20.51% (95% CI, 15.91% to 26.03%), 15.79% (95% CI, 11.39% to 21.47%), and 12.54% (95% CI, 6.38% to 23.18%) in 3, 6, and 12 months follow-up duration. Fatigue was the most common symptom, followed by respiratory symptoms, myalgia, and sleep disturbance. Patients with comorbidities had a significantly higher risk of experiencing long COVID [odds ratio (OR) = 1.72; 95% CI, 1.09 to 2.70; p = 0.019]. No statistically significant differences in sex, primary tumor, or tumor stage were detected.ConclusionNearly a quarter of cancer patients will experience long COVID after surviving from SARS-CoV-2 infection, and this would even last for 1 year or longer. Fatigue, respiratory symptoms, myalgia, and sleep disturbance need to be more addressed and managed to reduce symptom burden on cancer patients and improve quality of life. Patients with comorbidities are at a high risk of developing long COVID. Further randomized controlled trials with rigorous methodological designs and large sample sizes are needed for future validation.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023456665.

The demand for Chinese-Western medicine collaboration has grown significantly,but current integration methods have substantial limitations.This article analyzes core issues in developing and implementing synergistic Chinese-Western medicine clinical treatment strategies and explores the transformation from traditional integration to genuine synergistic models.We analyzed methodological obstacles in synergistic strategy development through literature review and theoretical analysis,and explored applications of intelligent technology in strategy development.Four core challenges were identified:(1)Treatment timing coordination difficulties caused by different decision-making approaches,with Chinese medicine using syndrome-based assessments and Western medicine relying on standardized measurements;(2)Treatment selection complexities when integrating different types of evidence,lacking frameworks for evaluating and combining diverse evidence sources;(3)Obstacles in incorporating patient preferences systematically,with inadequate assessment methods and unclear integration mechanisms;(4)Implementation barriers in translating synergistic strategies into clinical practice,requiring changes in organizational structures,workflows,and evaluation systems.Large language models(LLMs)and other intelligent technologies offer technical support for addressing these methodological challenges.This article examines current challenges in developing synergistic Chinese-Western medicine clinical strategies,analyzing the shift from traditional integration toward synergistic approaches and identifying four core methodological obstacles.Exploring intelligent technology applications provides insights to inform future research directions and clinical practice development in integrated healthcare delivery.

Objective:Whether large language models (LLMs) can effectively facilitate CM knowledge acquisition remains uncertain. This study aims to assess the adherence of LLMs to Clinical Practice Guidelines (CPGs) in CM. Methods:This cross-sectional study randomly selected ten CPGs in CM and constructed 150 questions across three categories: medication based on differential diagnosis (MDD), specific prescription consultation (SPC), and CM theory analysis (CTA). Eight LLMs (GPT-4o, Claude-3.5 Sonnet, Moonshot-v1, ChatGLM-4, DeepSeek-v3, DeepSeek-r1, Claude-4 sonnet, and Claude-4 sonnet thinking) were evaluated using both English and Chinese queries. The main evaluation metrics included accuracy, readability, and use of safety disclaimers. Results:Overall, DeepSeek-v3 and DeepSeek-r1 demonstrated superior performance in both English (median 5.00, interquartile range (IQR) 4.00-5.00 vs. median 5.00, IQR 3.70-5.00) and Chinese (both median 5.00, IQR 4.30-5.00), significantly outperforming all other models. All models achieved significantly higher accuracy in Chinese versus English responses (all p < 0.05). Significant variations in accuracy were observed across the categories of questions, with MDD and SPC questions presenting more challenges than CTA questions. English responses had lower readability (mean flesch reading ease score 32.7) compared to Chinese responses. Moonshot-v1 provided the highest rate of safety disclaimers (98.7% English, 100% Chinese). Conclusion:LLMs showed varying degrees of potential for acquiring CM knowledge. The performance of DeepSeek-v3 and DeepSeek-r1 is satisfactory. Optimizing LLMs to become effective tools for disseminating CM information is an important direction for future development.

BACKGROUND:Oral and plunging ranulas require effective treatment strategies to minimize recurrence; yet no consensus exists on the most effective approach. OBJECTIVES:This systematic review evaluated several treatments for the recurrence of oral and plunging ranulas. METHODOLOGY:A comprehensive search was conducted in five bibliographic databases and gray literature. Randomized and non-randomized studies were included if they investigated treatment approaches for oral or plunging ranulas. Two independent reviewers screened studies, extracted data, and assessed the risk of bias. The primary outcome was recurrence of (1) oral and (2) plunging ranula. For each type of ranula, a random-model frequentist network meta-analysis (NMA) was established for seven treatment strategies: enucleation, micromarsupialization, marsupialization, marsupialization with packing, partial sublingual gland excision, sublingual gland excision, and sublingual gland excision plus submandibular gland excision. A minimal important difference (MID) and the GRADE approach for NMA were used for interpretation of data. RESULTS:Eighteen studies were included (all non-randomized-14 for oral ranula and six for plunging ranula). No treatment demonstrated clear superiority in preventing recurrence. Certainty of evidence was low to very low for oral ranulas and very low for plunging ranulas, primarily due to the risk of bias, imprecision, and intransitivity. CONCLUSIONS:Given the low certainty of evidence, no single treatment can be considered superior to others. Future research should prioritize longer follow-up randomized controlled trials.

OBJECTIVE:To systematically review and meta-analyse the association between COVID-19 and the risk of mental disorders. METHODS:We searched PubMed, Embase, CINAHL, PsycINFO, and the reference lists of systematic reviews and included cohort studies assessing the association between COVID-19 and mental disorders. Two reviewers independently performed literature screening, data extraction, and the risk of bias assessment. We conducted a random-effect meta-analysis to assess the association and calculated the pooled risk ratio with 95 % confidence interval. RESULTS:Twenty-eight cohorts with a total of 977,434,207 participants proved eligible. We revealed a 40 % increased risk (RR = 1.40, 95 %CI: 1.23 to 1.59; absolute risk difference = 31 more per 1000 persons, 18 more to 45 more) of mental disorders in patients with COVID-19 compared with non-exposed individuals. Moreover, COVID-19 may be related to the risks of anxiety or fear-related disorders, mood disorders, bipolar or related disorders, depressive disorders, unspecified mood disorders, neurocognitive disorders, dementia, mild cognitive disorders, unspecified neurocognitive disorders, psychotic disorders, stress and adjustment disorders, post-traumatic stress disorder, unspecified stress and adjustment disorders, and prescriptions for psychotropic medications. CONCLUSIONS:Our findings suggest an association between COVID-19 and risk of mental disorders, as well as the prescriptions for psychotropic medications. It is imperative for individuals to become vigilant of mental health problems that may arise following COVID-19.

BACKGROUND:Ankyloglossia is a lingual frenulum alteration that restricts tongue movements and may be related to breastfeeding difficulties. This study aimed to determine the estimated proportion of ankyloglossia among infants with breastfeeding difficulties. METHODS:The protocol was registered in PROSPERO (#CRD42023394743). Eight electronic databases were searched up to March 20th, 2023, and updated to January 14th, 2025. Inclusion criteria were observational studies that evaluated ankyloglossia among infants up to 24 months with breastfeeding difficulties, using any assessment tool to diagnose ankyloglossia and breastfeeding difficulties. The risk of bias was assessed using the Joanna Briggs Institute (JBI) appraisal tool for prevalence studies. The estimated proportion of ankyloglossia and 95 % confidence intervals (CI) were calculated through a random-effect model meta-analysis of proportion. RESULTS:Twelve studies (ten cross-sectional, two cohort) with 8538 infants were included. The overall estimated proportion of ankyloglossia among breastfeeding difficulties was 34 % (95 %CI: 12 %-61 %) using an ankyloglossia and a breastfeeding difficulties assessment tool. The estimated proportion varied from 9 % using UNICEF Breastfeeding Assessment and Observation Protocol (95 %CI: 0-34 %) to 74 % (95 %CI: 60 %-84 %) using LATCH Breastfeeding Assessment Tool. The estimated proportion of breastfeeding difficulties was 30 % (95 %CI: 19 %-43 %). CONCLUSIONS:Close to 34 % of infants with breastfeeding difficulties have ankyloglossia, and the overall estimated proportion of breastfeeding difficulties was around 30 %. The proportion may vary depending on the assessment tool used. As seven out of 10 infants had breastfeeding difficulties not due to ankyloglossia, clinicians should carefully evaluate other causes that impact breastfeeding before referring the patient to surgical interventions.