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PubMed2026

From pathobiology to prescribing in obesity-driven HFpEF: A systematic review and practical therapeutic framework.

Heart failure with preserved ejection fraction (HFpEF) is increasingly driven by obesity and cardiometabolic dysfunction. In this phenotype, the dominant biology extends beyond congestion alone and includes visceral and epicardial adiposity, systemic inflammation, impaired myocardial energetics, endothelial dysfunction, and exertional elevation in filling pressures. We performed a PRISMA-compliant systematic review with structured narrative evidence synthesis to evaluate pharmacological therapy in obesity-driven HFpEF, searching PubMed/MEDLINE, Scopus, Web of Science Core Collection, ClinicalTrials.gov, and WHO ICTRP through December 2025. Eighteen reports were included in the final qualitative synthesis. The available evidence supports sodium-glucose cotransporter 2 inhibitors as the pharmacological foundation because they provide the most mature outcome data across the preserved ejection fraction spectrum. Semaglutide improves symptoms, physical limitations, exercise capacity, and body weight in dedicated obesity-related HFpEF trials, whereas tirzepatide extends this signal by improving clinical status and reducing worsening heart failure events. Finerenone broadens the therapeutic platform in HF with mildly reduced or preserved ejection fraction, although obesity-specific data remain indirect. Conventional neurohormonal therapies retain a selective role, but they are not the principal biological match for this phenotype. Obesity-driven HFpEF should therefore be managed as a cardiometabolic syndrome with heart failure expression, using a phenotype-based sequence that links diagnosis, decongestion, SGLT2 inhibition, obesity-directed therapy, and selective adjunctive intensification.

Humans↗

PubMed2026

Multimodal intervention benefits: Responder analysis of J-MINT PRIME Kanagawa trial.

INTRODUCTION: The J-MINT PRIME Kanagawa trial was an 18-month multimodal intervention (incorporating exercise, nutrition, and metabolic management) for dementia prevention. Because the primary analysis showed no significant benefits, we performed an exploratory responder analysis to identify responsive subpopulations. METHODS: We analyzed the Full Analysis Set comprising 188 participants. Classification and regression tree (CART) analysis, applied to the intervention arm, identified baseline predictors of cognitive improvement. These rules were then applied to the entire cohort to evaluate treatment effects on the Mini-Mental State Examination (MMSE) using fully adjusted mixed-effects models for repeated measures (MMRM). RESULTS: CART identified a "Target Group" (N = 108) characterized by baseline profiles such as an MMSE score < 28 or specific metabolic ranges (e.g., LDL-C < 135 mg/dL). Within this target group, the intervention significantly preserved MMSE trajectories compared with the control group (group × time interaction, P = 0.022). In contrast, the Non-Target Group (N = 80), consisting of high-functioning individuals (MMSE ≥ 28), exhibited no significant group × time interaction. DISCUSSION: Multimodal interventions may effectively preserve global cognition in older adults with sub-threshold cognitive decline. Careful targeting of appropriate populations, while considering potential longitudinal measurement artifacts (e.g., practice effects), is essential. These findings provide a hypothesis-generating framework that warrants external validation in future prevention trials.

Humans↗

PubMed2026

The future of precision oncology and artificial intelligence in Belgium: scenarios and policy responses.

PURPOSE: Precision medicine, also known as personalized medicine, enables the provision of tailored health services to patients. In the prevention, early detection, and treatment of cancers, precision medicine is highly promising, given the increasing use of genomic profiling for diagnosis and adapting therapies in several tumor types. Artificial Intelligence (AI) can support this process by analyzing vast amounts of relevant data. However, high-quality data and financial investments in the health system are essential for the implementation of precision medicine and AI solutions in routine cancer care. DESIGN/METHODOLOGY/APPROACH: Building on the quantitative outcomes of a foresight exercise published in another study, this article collects qualitative data to gain more detailed insights into the future of precision oncology in Belgium and discusses the role of AI in this field. It reports the results of a series of expert workshops, focusing on four hypothetical future scenarios that are centered around technological and economic issues that must be overcome for the widespread use of precision oncology in Belgium. FINDINGS: The study concludes that all four scenarios discussed in the workshops would require supportive policy measures in Belgium, which should go beyond mere technological and economic considerations, such as involving patient associations and the public in policy design or creating multi-disciplinary expert groups for precision medicine. ORIGINALITY/VALUE: To the best of our knowledge, this is the first study to employ foresight methodology to illustrate possible future scenarios, scrutinize feasible approaches for implementing precision oncology in Belgium, and discuss the use of AI in this context.

Belgium↗

PubMed2026

Implementing a novel digital health platform for self-management of postmenopausal osteoporosis: A qualitative study of user experiences, perspectives and implementation outcomes.

BACKGROUND: Osteoporosis self-management requires scalable support, and digital health platforms may meet this need. This study aimed to characterise the experiences and perspectives of postmenopausal women who participated in a 12-month randomised controlled trial (RCT) of a digital voice assistant (DVA) delivered osteoporosis self-management intervention, and to assess key implementation outcomes. METHODS: This was a qualitative analysis of interviews with postmenopausal women from the intervention arm (DVA group) of the RCT. The DVA program broadcast education videos, medication reminders, home-based exercise, nutrition advice and monthly quizzes through a DVA device. Semi-structured interviews were recorded, transcribed and managed in NVivo through reflexive thematic analysis, guided by the Practical Planning for Implementation and Scale-Up and Proctor's implementation outcome taxonomy frameworks. Evidence weighting summarised participant coverage and code density. RESULTS: Twenty-two of 25 (88%) DVA group participants completed semi-structured interviews. Thematic analysis identified seven themes mapped to Proctor's implementation outcomes. Evidence weighting indicated strong support for the intervention's appropriateness and acceptability, moderate support for its adoption, fidelity, feasibility and sustainability, and limited support for costs. Participants valued clear audiovisual guidance, conversation-based interactions with natural language, and flexible home-based access to self-management. CONCLUSION: Digital health platforms for osteoporosis self-management appear feasible, acceptable and sustainable among postmenopausal women. Findings indicate that these platforms are approaching readiness for evaluation in implementation-focused settings, contingent on streamlined content, reliable delivery modalities, accessible user support, clear privacy regulations and pragmatic pricing models.

Humans↗

PubMed2026

Multimodal alignment improves generalizability of genomic biomarker prediction in computational pathology.

Computational pathology models that use digitized histopathology whole-slide images have the potential to become a cost-effective and scalable alternative to molecular assays for the prediction of genomic biomarkers, a key task in precision oncology. However, as new genomic biomarkers are discovered or quantified, large, labeled datasets must be prospectively collected to train new models. To address this challenge, we developed multimodal alignment for biomarker learning and generalization (MARBLE), a multimodal contrastive pretraining strategy that integrates structured biomarker knowledge into representation learning of histopathology images. MARBLE aligns histopathology-derived representations with representations of genomic biomarkers generated by a large language model (LLM) and a protein language model (PLM). This biologically informed alignment enables data-efficient generalization to novel, out-of-distribution biomarkers. Using the MSK-IMPACT cohort of over 40,000 patients across multiple biomarker panel versions, we design experiments grounded in real-world data to demonstrate the value of our proposed approach.

CP: computational biology↗

PubMed2026

A Meta-learning-driven strategy for adulteration detection in sweet potato starch and vermicelli using Raman spectroscopy.

To address the widespread adulteration of sweet potato starch and its vermicelli with cheaper starches and overcome conventional supervised learning's dependency on large labeled datasets, this study developed a few-shot discrimination method integrating Raman spectroscopy with meta-learning. We constructed a meta-learning framework using cassava- and wheat-adulterated sweet potato starch as the source domain for training, with potato-adulterated sweet potato starch and cassava-adulterated sweet potato vermicelli as two target domains for testing. Raman spectra showed high consistency between sweet potato vermicelli and its raw starch, laying the foundation for cross-domain detection. Testing yielded comprehensive classification accuracies of 95.33% and 98.00% for the two target domains, significantly outperforming SVM, RF, and CNN (max. 85.24%). This approach effectively identifies subtle starch variety differences in complex adulteration, providing novel food quality inspection solutions and verifying the feasibility of raw material-to-finished product cross-domain detection.

Ipomoea batatas↗

PubMed2026

Final-year nursing students' clinical practice experiences: a reflection study.

OBJECTIVES: This study aimed to explore the most impactful clinical practice experiences of final-year nursing students and the future-oriented actions developed in response to these experiences. METHODS: A retrospective descriptive qualitative design was used. Following reflection training in the internship practice course, 134 final-year nursing students were asked to describe the experience that affected them most during clinical practice. A total of 123 written reflections were analyzed using content analysis. RESULTS: Three themes emerged: near-miss events, incivility behaviors, and positive preceptoring roles. Negative experiences were mainly related to patients, relatives, and nurses and often led students to feel fear and inadequacy. Students reported action plans focused on effective communication, safe patient care, and becoming positive role models. CONCLUSIONS: These findings highlight the importance of supportive clinical learning environments and positive professional socialization during the transition from student to nurse. IMPLICATIONS FOR INTERNATIONAL AUDIENCE: Nursing students worldwide may encounter incivility and near-miss events during clinical practice, potentially adversely affecting their learning experiences and professional development.

Humans↗

PubMed2026

Prevalence of POMC allele associated with obesity in feline population in Vietnam.

Feline obesity is an increasingly important health problem influenced by both genetic predisposition and husbandry practices. This study investigated the prevalence and phenotypic relevance of the feline proopiomelanocortin (POMC) c.28G > C (p.Gly10Arg) variant in a Vietnamese cat population and evaluated environmental factors associated with obesity. A total of 63 clinically healthy cats were classified as normal weight (body condition score [BCS] 5-6/9; n = 40) or obese (BCS ≥7/9; n = 23). Genotyping was performed using a newly developed PCR-restriction fragment length polymorphism assay and validated by Sanger sequencing. Ventral subcutaneous adipose tissue (VSAT) thickness was measured ultrasonographically as an objective indicator of adiposity. Genotyping revealed a high prevalence of the risk-associated C allele, with 45/63 cats (71.4%) carrying the GC genotype and 18/63 (28.6%) carrying the CC genotype, whereas the GG genotype was not detected, giving a Callele frequency of 64.3%. Obese cats had significantly greater body weight and VSAT thickness than normal-weight cats. Neuter status and ad libitum feeding were significantly associated with obesity, whereas diet type, housing, exercise frequency, and begging behavior were not. Although genotype distribution did not differ significantly between normal and obese cohorts, obese CC cats showed significantly greater VSAT thickness than obese GC cats, indicating an allele-dosage effect on adiposity. These findings suggest that the POMC c.28G > C variant is a useful risk-informative marker and that combining genetic screening with objective fat assessment may support earlier identification and prevention of feline obesity in Vietnam under routine laboratory conditions and guide personalized management strategies in practice.

Animals↗

PubMed2026

Determinants of Nonspecific Response to Treatment in Randomized Controlled Trials of Major Depressive Disorder: A Narrative Review.

The design, conduct, and interpretation of double-blind randomized placebo-controlled clinical trials in major depressive disorder (MDD) are complicated by determinants of nonspecific response to treatment (NSRT). This narrative review provides a comprehensive overview of the determinants of NSRT in randomized controlled trials (RCTs) for MDD, including the placebo effect, factors related to measurement of the primary endpoint, the inclusion of misdiagnosed patients, the relapsing-remitting course of MDD, and factors related to functional unblinding. Potential strategies to reduce the impact of the determinants of NSRT and to improve the interpretation of RCT outcomes in MDD are also summarized. These strategies include use of centralized rating and standardized rater training, independent diagnostic confirmation, optimized site selection, minimizing financial incentives, exclusion of subjects participating in multiple clinical trials, exclusion of patients with unstable major depressive episode trajectories, and use of active placebo and alternative trial designs. Uniformity among experts in the definitions of determinants of NSRT and related concepts, as well as in strategies to address them, may facilitate progress in the development of novel treatments for MDD.

Humans↗

PubMed2026

Effect of knee and hip joint positions on passive stiffness of the rectus femoris and vastus lateralis in healthy individuals.

Passive muscle stiffness is a key determinant of musculoskeletal function and is influenced by structural components such as titin, connective tissue, and fascia. However, the effects of joint position, muscle depth, and sex on quadriceps passive stiffness remain unclear. To investigate the passive stiffness of the rectus femoris (RF) and vastus lateralis (VL) under different joint configurations, muscle depths, and between sexes using shear wave elastography (SWE). Thirty-six healthy young adults (18 men and 18 women) participated in this randomized crossover study. Passive stiffness was assessed in four positions of knee flexion: supine with 60° (SUP60), supine with 20° (SUP20), sitting with 60° (SIT60), and sitting with 20° (SIT20). SWE measurements (m/s) were obtained from 30 regions of interest (ROIs) per muscle, categorized into superficial, intermediate, and deep levels. Data were analyzed using Generalized Estimating Equations (GEE). A significant effect of position was observed, with higher stiffness values in the SUP60 condition for both RF and VL (p < 0.001). Superficial regions consistently exhibited greater stiffness compared to intermediate and deep regions across all positions (p < 0.001). Additionally, men demonstrated significantly higher stiffness values than women (p < 0.001). Significant interactions were found between position and muscle, as well as position and depth. Quadriceps passive stiffness is influenced by joint position, muscle depth, and sex. The SUP60 position elicits the highest stiffness, while superficial muscle regions are consistently stiffer. These findings highlight the non-uniform mechanical behavior of the quadriceps and may have implications for clinical assessment, rehabilitation, and exercise prescription. Clinical trial registration: This study was registered at Clinicaltrials.gov in June 06th, 2023. Register number NCT05905406. Link to access https//clinicaltrials.gov/study/NCT05905406.

Humans↗

PubMed2026

Workplace Safety Champions: Strengthening safety culture through nurse engagement.

Workplace violence is a growing concern in health care, disproportionately affecting frontline nurses and nursing assistants. Despite high prevalence, underreporting remains a barrier to effective prevention and response. This article describes the development, implementation, and outcomes of a Workplace Safety Champion program designed to increase reporting of violent incidents and strengthen a culture of safety. A multidisciplinary task force developed an evidence-based Workplace Safety Champion course that emphasizes de-escalation strategies, reporting processes, and staff support. Champions were appointed across inpatient and emergency units and integrated into a hospital-wide Workplace Safety Champion Council. Program evaluation used course completion data and posttraining surveys. The organizational goal of having at least one trained champion in 90% of inpatient and emergency units was exceeded, with 98% of units represented (N = 93 champions). More than 85% of learners reported intent to change their response to workplace violence, and 90% endorsed improved knowledge of resources and de-escalation strategies. The Workplace Safety Champion program successfully improved staff awareness, reporting, and engagement in workplace violence prevention. Embedding champions across units can serve as a sustainable strategy to strengthen safety culture and support frontline health care workers.

Humans↗

PubMed2026

The effects of nitrate and nitrite supplementation on mitochondrial respiration in permeabilized muscle fibres in young healthy adults.

Nitric oxide (NO) is a direct regulator of mitochondrial respiration. Nitrate (NO3-) and nitrite (NO2-) are good sources of NO, but whether their effects on mitochondrial respiration differ between in vivo and in vitro administration remains unclear. In Study 1, 8 participants consumed NO3- -rich beetroot juice (BR) (∼12.8 mmol NO3-) and NO3- -depleted placebo beetroot juice (PL) (∼0.08 mmol NO3-) acutely and chronically for 2 weeks in a randomised, double-blind, crossover design. A substrate-uncoupler-inhibitor titration (SUIT) protocol was used to assess mitochondrial respiration using high-resolution respirometry (oxygen tension: ∼200-450 μM) in permeabilized muscle fibres. In Study 2, skeletal muscle samples were collected from 11 participants. In a randomised, crossover design, different doses (0, 1.5, and 3.0 μM) of sodium nitrite (NaNO2) were administered to permeabilized muscle fibres. Mitochondrial respiration was measured using the same SUIT protocol under lower oxygen tension (∼50-200 μM). Although muscle NO3- concentration significantly increased after both acute and chronic BR supplementation, mitochondrial respiration and exercise performance did not differ between PL and BR in either condition. Similarly, absolute oxygen flux across different respiratory states were not different between different doses of NaNO2. However, the leak control ratio, reflecting the degree of uncoupling of mitochondrial respiration, was significantly higher with 3.0 μM NaNO2 administration (0.12 ± 0.05) compared to 0 μM NaNO2 administration (0.09 ± 0.04, P = 0.03). These findings, involving both in vivo and in vitro administration approaches, albeit in the presence of relatively high oxygen concentrations, suggest that neither NO3- nor NO2- improves mitochondrial respiration, at least in young healthy adults.

Humans↗

PubMed2026

An exploratory analysis of decision-making in population affinity estimation among forensic anthropology practitioners in the United States.

Population affinity estimation in forensic anthropology often involves the integration of multiple pieces of information, including visual (nonmetric) and metric data. This study examines how practitioners interpret and synthesize visual and metric information and their decision-making processes. A Qualtrics survey was developed using two cases: Case 1 presented clear nonmetric signal but ambiguous metric signal, while Case 2 showed more ambiguous nonmetric signal but clear metric signal. Practitioners were asked to estimate population affinity based on visual assessment, Fordisc data, and provide a final, integrated assessment. A total of 22 valid survey responses were received, with the majority of survey respondents reporting more than 10 years of forensic anthropology experience and holding a PhD degree. Results showed that there is substantial variability in Fordisc use and interpretation. Across both cases, participants synthesized conflicting visual and metric information, converged toward the stronger signal, and came to more consistent final estimates relative to the more ambiguous input. These findings highlight variability in practitioner decision-making but suggest that integration of nonmetric and metric information in population affinity estimation can moderate decision-making uncertainty. The results have implications for forensic anthropology education, training, and proficiency testing.

Humans↗

PubMed2026

Mul-PheG2P: decoupled learning and prediction-space fusion enables robust and interpretable multi-phenotype genomic prediction.

Genomic prediction of multiple phenotypes is crucial in modern plant breeding; however, existing methods struggle with negative transfer and lack interpretability, particularly across high-dimensional small-sample data and diverse species. To address this, we propose Mul-PheG2P, a novel paradigm based on decoupled learning and predictive space fusion. It employs a two-stage design: first training phenotype-specific encoders using genetic data, then decoupling phenotype-specific learning from cross-phenotype aggregation via an interpretable prediction layer. Mul-PheG2P outperforms existing methods across diverse crop datasets, including maize (Zea mays), wheat (Triticum aestivum), and tomato (Solanum lycopersicum). It provides a multi-scale interpretability chain: at the macro level, it quantifies phenotypic contributions via attention-based weighting; at the micro level, Integrated Gradients reveal the genetic basis of predictions. Notably, the model successfully identified the CCT (CONSTANS, CO-like, and TOC) motif regulating photoperiodism and the SQUAMOSA (SQUAMOSA promoter binding protein) promoter for inflorescence development, confirming its ability to capture functional biological mechanisms. These results highlight the high performance and interpretability of Mul-PheG2P, showcasing its value for low-cost, large-scale screening to advance precision breeding.

Phenotype↗

PubMed2026

Assessing AI literacy and attitudes among medical students: implications for integration into healthcare practice.

PURPOSE: This study aims to assess AI literacy and attitudes among medical students and explore their implications for integrating AI into healthcare practice. DESIGN/METHODOLOGY/APPROACH: A quantitative research design was employed to comprehensively evaluate AI literacy and attitudes among 374 Lusaka Apex Medical University medical students. Data were collected from April 3, 2024, to April 30, 2024, using a closed-ended questionnaire. The questionnaire covered various aspects of AI literacy, perceived benefits of AI in healthcare, strategies for staying informed about AI, relevant AI applications for future practice, concerns related to AI algorithm training and AI-based chatbots in healthcare. FINDINGS: The study revealed varying levels of AI literacy among medical students with a basic understanding of AI principles. Perceptions regarding AI's role in healthcare varied, with recognition of key benefits such as improved diagnosis accuracy and enhanced treatment planning. Students relied predominantly on online resources to stay informed about AI. Concerns included bias reinforcement, data privacy and over-reliance on technology. ORIGINALITY/VALUE: This study contributes original insights into medical students' AI literacy and attitudes, highlighting the need for targeted educational interventions and ethical considerations in AI integration within medical education and practice.

Students, Medical↗

PubMed2026

Effects of intensive lifestyle interventions with calorie-carbohydrate-restricted diet versus time-restricted eating on appetite and binge eating in type 2 diabetes: A randomized controlled trial.

The impact of intensive lifestyle interventions on appetite regulation and binge eating in individuals with type 2 diabetes (T2D) remains unclear. This study evaluated the effects of combined lifestyle interventions on appetite responses and binge eating in overweight or obese adults with T2D. In a randomized trial, 120 participants with T2D were allocated to three groups (n = 40 each): (1) Calorie-carbohydrate restriction (CCR), (2) Time-restricted eating with CCR (TRE + CCR), or (3) Control. Intervention groups received structured exercise and behavioral education based on the Information-Motivation-Behavioral Skills model. Appetite perceptions (hunger, satiety, desire to eat, and prospective food consumption) and binge eating (Binge Eating Scale; BES and objective binge episodes) were evaluated at baseline, week 12, and week 24 using linear mixed models. Both CCR and TRE + CCR significantly improved subjective appetite compared with the control group at 12 and 24 weeks (all p < 0.01). At 24 weeks, hunger decreased by -24.1 mm (95% CI: -35.8, -12.5) in the CCR group and -32.7 mm (95% CI: -44.2, -21.3) in the TRE + CCR group. Satiety also increased by 21.7 mm (95% CI: 9.46, 33.9) and 29.4 mm (95% CI: 17.4, 41.4), respectively. Significant reductions were observed for desire to eat and prospective food consumption. In contrast, changes in BES and objective binge episodes were not significantly different between groups at any time point. No significant differences were detected between the CCR and the TRE + CCR groups. Intensive lifestyle interventions incorporating CCR or TRE + CCR effectively reduced appetite in adults with T2D but did not significantly affect binge eating. Future research should target individuals with higher baseline BES scores to clarify potential benefits for binge eating behavior.

Humans↗

PubMed2026

Behind the Curtain of Care. Nurses' Experiences Providing Care to Consumers With Alcohol and Other Drug Issues: A Qualitative Scoping Review.

AIM: To scope and synthesise qualitative literature relating to nurses' experiences of providing care to consumers with alcohol and other drug issues and explore how meaning is constructed in practice. DESIGN: Scoping review. METHODS: A scoping review was conducted following Arksey and O'Malley's framework. Findings were analysed using thematic analysis. DATA SOURCES: Systematic searches were conducted between September and November 2025 across Medline, Emcare, CINAHL and Google Scholar, using controlled vocabulary and keywords relevant to nurses' experiences of providing care to consumers with alcohol and other drug issues. RESULTS: Twenty-four studies from 12 countries were included. Seven themes were identified: emotional aspects of care, education, training and skills in practice, the spectrum of stigma, ethical issues in professional practice, navigating pain management, limited support, and how meaning is constructed in practice. CONCLUSION: Nurses' experiences of providing care to consumers with alcohol and other drug issues are shaped by multiple intersecting factors influencing care delivery and professional practice. Further research is needed to examine how workplace culture, language and interpersonal interactions influence healthcare experiences, and inform education, service development and support needs. REPORTING METHOD: Reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

alcohol and other drugs↗

PubMed2026

Artificial Intelligence Technologies in Nursing Clinical Decision-Making: An Umbrella Review.

AIM: To describe contemporary peer-reviewed literature on artificial intelligence in nurses' clinical decision-making. METHODS: An umbrella review of literature reviews. DATA SOURCES: Four major databases were searched for reviews published between 2019 and 2024. RESULTS: Sixteen literature reviews reported on 965 nursing artificial intelligence primary studies. The studies focused on technology development and emerging performance evaluations, whilst real-world testing or implementation in nursing clinical settings was rare. Rigorous comparative analyses were lacking. While artificial intelligence demonstrates promise in decision-making, challenges such as a lack of controlled studies, algorithmic bias, limited reproducibility and insufficient clinical trials hinder its practical impact. Ethical concerns, transparency and patient data privacy issues pose barriers to AI integration in nursing practice. Ethical and legal guidelines for patient privacy are needed and should be taught along with AI literacy training for nurses. CONCLUSIONS: Artificial intelligence has the potential to enhance clinical nursing decision-making, although evidence is limited by too few examples of nurse participation during development. Underutilisation in administrative nursing functions hinders implementation. Nurses should assume a central role in the design and development of AI applications to ensure that these technologies address the realities of nursing practice. With such improvements, artificial intelligence can transform nursing practice, improve nurses' clinical decision-making and ultimately enhance consumer healthcare outcomes. PATIENT OR PUBLIC INVOLVEMENT: No Patient or Public Involvement. REPORTING METHOD: While there is no reporting checklist for umbrella reviews, the PRISMA guide for systematic reviews was followed.

Artificial Intelligence↗