Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 12 appraisals
Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences
Deep fused explainable neural architecture for the automatic recognition of therapeutic medicinal plants
BACKGROUND: Medicinal plants have been utilized for centuries for their beneficial properties and significant impact on traditional medical systems worldwide. Indian medicinal plants and their therapeutic properties have recently gained increased attention from the global healthcare community. Accurate identification and classification of medicinal plants ensure proper therapeutic applications. However, botanists manually identify these plants, which is subjective, labor-intensive, and time-consuming. OBJECTIVE: The main objective of this study is to develop an accurate and automated classification of these medicinal plants for diverse applications such as conservation initiatives, quality assurance, and the formulation of novel herbal treatments. METHODS: A Deep Fused Explainable Neural Architecture termed MedPlantNet is proposed for robust recognition of medicinal plant leaves. The deep discriminative features are extracted from the medicinal plant images using a fine-tuned Deep Convolutional Neural Network (DCNN). A customized Support Vector Machine is developed to efficiently classify these extracted deep features. To enhance model interpretability, a meta-visualization framework incorporating Grad-CAM and Occlusion Sensitivity visualization is implemented to identify the regions of the image that contribute to the classification decisions. We utilized the Mendeley benchmark Medicinal plant leaf database with 30 classes to train and evaluate the model. RESULTS: To assess the effectiveness of the proposed model, accuracy, specificity, sensitivity, and AUC are calculated. The proposed system outperforms existing state-of-the-art (SOTA) algorithms for medicinal plant classification, achieving an optimal maximum validation accuracy of 99.87% and AUC of 1. To further validate the generalizability external validation was performed on Bangladeshi medicinal plant dataset and MedLeaves dataset where the proposed model MedPlantNet attained 99.27% and 99.80% validation accuracy respectively. CONCLUSION: MedPlantNet provides an automatic framework for medicinal plant identification based on deep feature extraction, classification and explainable visualization techniques. The results obtained indicate its potential utility in supporting biodiversity conservation, quality assurance and herbal medicine research.
13 July 2026
Read appraisal →Journal of the American College of Clinical Pharmacy : JACCP
Research and Scholarly Methods: Using Artificial Intelligence
Artificial intelligence (AI) is quickly changing health care and expanding research opportunities, including in pharmacy. AI can offer effective solutions for numerous challenges in pharmacy research, such as persistent gaps in evidence-based prescribing, slow drug discovery-to-adoption timelines, complex data environments, and the need for more efficient methods, including qualitative methods. AI tools-ranging from machine learning models to large language models (LLMs)-offer new capabilities for data analysis, prediction, and content generation. Pharmacy researchers can harness these technologies to address persistent gaps in medication management, optimize patient safety and outcomes, and streamline many research workflows. However, successful integration requires understanding the spectrum of AI capabilities, appropriate use cases, and potential limitations. AI must be tailored to the research question, data, and institutional resources. This paper will guide pharmacy researchers in identifying use cases for AI in their research, selecting and applying AI tools, mitigating risks, providing standards for reporting the use of AI in research, and maximizing benefits for scientific discovery and patient outcomes.
1 July 2026
Read appraisal →BMJ open
Effectiveness and safety of pharmacist prescribing: a systematic review
OBJECTIVE: To examine the effectiveness and safety of pharmacist prescribing across multiple healthcare settings. DESIGN: A systematic review of quantitative studies. ELIGIBILITY CRITERIA FOR THE SELECTION OF STUDIES: Quantitative studies assessing the effectiveness and safety of pharmacist prescribing compared with non-pharmacist prescribing in any healthcare setting and for any healthcare population. A clear statement of pharmacists' prescriptive authority was required for inclusion in this systematic review. DATA SOURCES: A systematic search was conducted using six electronic databases: Embase (Ovid), MEDLINE (EBSCO), SCiELO, Dimensions AI, Cochrane Library and Epistemonikos. Database searches were conducted from database inception to 29 January 2025. Additional grey literature searches were conducted using Google and DuckDuckGo. Both backward and forward citation chasing were conducted for all included studies. DATA EXTRACTION AND SYNTHESIS: Data were extracted using standardised bespoke data extraction forms. The revised Cochrane Risk of Bias 2 tool for randomised controlled trials and the Risk of Bias in Non-Randomised Studies of Interventions tool were used to assess risk of bias. A narrative synthesis approach was applied following the Synthesis Without Meta-analysis guideline. The Grading of Recommendations Assessment, Development and Evaluation approach was used to assess the level of certainty of the evidence. RESULTS: Of the 39 included studies, 32 studies reported on effectiveness and 20 studies reported on safety across 15 health conditions. Healthcare settings included outpatient (n=14), primary care (n=10), community pharmacy (n=6), inpatient (n=5), emergency department (n=1) and long-term care (n=3). These studies were based in the USA (n=26), Canada (n=5), the UK (n=4), Australia (n=2) and Singapore (n=2). In total, there were 153 outcomes related to safety and effectiveness. For 74 outcomes, no significant difference was reported between pharmacist prescribing and non-pharmacist prescribing, while 46 outcomes were significantly improved with pharmacist prescribing. Four outcomes reported in favour of non-pharmacist prescribing. Inferential statistics were not reported for 29 outcomes, meaning we cannot comment on their statistical significance. The certainty of evidence was low or very low for all outcomes. CONCLUSIONS: The consistency of effectiveness and safety findings across studies, showing either no significant difference (indicating equivalence of care and outcomes) or significant improvement in pharmacist prescribing groups, suggests it is a potential policy option. Future research on implementation, public and patient preferences and cost-effectiveness would provide valuable insights into the potential benefits of pharmacist prescribing at a health system level.
28 June 2026
Read appraisal →European journal of hospital pharmacy : science and practice
A bibliometric analysis of the application trends of information technology in antimicrobial stewardship within hospitals
OBJECTIVES: This study uses bibliometric analysis to systematically map research trends, knowledge structure and evolution of information technology (IT) in hospital antimicrobial stewardship (AMS) over the last 20 years. METHODS: A Web of Science Core Collection search (2000-2025) yielded 258 English-language publications on IT applications in hospital AMS. A bibliometric analysis, utilising CiteSpace and Bibliometrix, quantitatively evaluated domain evolution through temporal analysis, network mapping, keyword clustering, burst detection and examination of highly cited publications. RESULTS: The bibliometric analysis reveals a fluctuating yet overall increasing trend in annual publications, peaking at 49 articles in 2024. The US (131 publications) and European nations demonstrate significant research output (centrality >0.2), with major collaborative networks coalescing around institutions including Harvard University and Imperial College London. Keyword analysis identifies 'AMS' as a core theme, closely associated with technological keywords such as 'machine learning (ML)', 'clinical decision support systems (CDSS)', 'electronic health records (EHR)' and 'artificial intelligence (AI)'. Emerging trends suggest a shift in research focus from foundational strategies to data-driven prediction of antimicrobial resistance (AMR) and precision interventions. Highly cited literature emphasises the integration of EHR and ML technologies for optimising prescriptions and predicting resistance patterns. CONCLUSIONS: IT-driven AMS research has shifted from empirical management to data science. Despite EHR integration, and ML and CDSS support, challenges remain in data standardisation, technical deployment and ethics. Future work must emphasise global collaboration, standardisation, design refinement and ethical guidelines, and provide clear algorithm explanations to enhance AMR mitigation.
26 June 2026
Read appraisal →Journal of medical Internet research
Receipt of Medicines Information From the Internet and Other Information Sources Among Adult Medicine Users in Developed Economies, 2010-2025: Systematic Review
BACKGROUND: The internet, social media, and digital health tools have transformed access and receipt of medicines information (MI), complementing or replacing traditional sources, such as physicians, pharmacists, and package leaflets. OBJECTIVE: This study aimed to examine the prevalence and patterns of adult medicine users' receipt of MI from electronic sources (eg, internet and social media) compared with traditional sources, and to assess trends in the receipt of MI in developed economies since 2010. METHODS: A systematic search was conducted in CINAHL, the Cochrane Library, ProQuest, Scopus (including Embase and MEDLINE), and Web of Science databases for peer-reviewed studies conducted and published between January 1, 2010, and December 31, 2025. Studies including adults (≥18 years) in developed economies were eligible. The review followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. Methodological quality and risk of bias were assessed using the Mixed Methods Appraisal Tool. Due to substantial heterogeneity, findings were synthesized narratively without meta-analysis. RESULTS: Twenty-six studies from 10 countries, involving 19,891 medicine users, were included. Most studies were cross-sectional surveys (n=19), with only one being a national long-term trend study. Physicians, pharmacists, and package leaflets consistently remained the most common MI sources, irrespective of patient group, country, or study design. No clear temporal trends in MI receipt were observed. Variations in the frequency of MI receipt were evident across research methods, medicine user groups, and types of medicines. Individuals with greater familiarity with the internet were more likely to receive MI from electronic sources, such as websites and social media. In most studies, MI obtained from social media was not distinguished from other internet-based sources. Half of the studies focused on heterogeneous medicine user groups (n=14), while the remainder examined specific medicine user groups (n=12). Although the methodological quality of the studies was generally acceptable, only 3 studies reported the use of an explicit theoretical framework. CONCLUSIONS: Traditional MI sources remain central for adult medicine users despite the growing role of electronic platforms. While the receipt of MI from electronic sources appears more common among internet-experienced users, no significant temporal trends in using these sources were identified. Further research is needed to better distinguish between different digital MI sources, including artificial intelligence-based MI sources, and to explore their evolving roles, implications for health-related decision-making, and associations with user characteristics and health contexts in the digital era. This review is innovative in systematically comparing traditional and electronic MI sources across developed economies and in highlighting methodological and conceptual gaps in existing research. By synthesizing cross-national evidence and identifying the need for clearer differentiation of digital MI subtypes, the study contributes to the field and informs the development of more targeted, reliable, and user-centered MI strategies in real-world health care settings.
22 May 2026
Read appraisal →Journal of medical Internet research
AI-Based Automation for Medication Reconciliation: Scoping Review
BACKGROUND: Medication reconciliation (MedRec) has the potential to improve patient safety by enhancing the continuity of medication information across settings. MedRec involves 3 core tasks: the creation of a best possible medication history, the identification of medication discrepancies among medication lists, and the resolution of medication discrepancies. While artificial intelligence (AI) has the potential to improve MedRec, existing reviews have not identified the ways in which researchers have used AI to facilitate MedRec tasks and their constituent subtasks or the level of automation achieved. OBJECTIVE: This scoping review aimed to map how previous research has applied AI to MedRec tasks and subtasks and assess the extent of automation achieved. METHODS: We searched MEDLINE, Embase, Web of Science, IEEE Xplore, and Compendex in June 2024 for studies that used AI to support a MedRec task or subtask, excluding entirely rule-based tools or studies focused on other aspects of medication management. After screening 2345 unique records, we conducted backward citation searching of studies included at the full-text stage, identifying an additional 795 unique records. We used a 4-stage model of human information processing as a structural lens to guide our considerations of automation, mapping the core tasks of MedRec onto this model. RESULTS: A total of 94 studies met the inclusion criteria. All studies addressed subtasks related to the creation of a best possible medication history. Only 2.1% (n=2) of the studies also addressed the identification of discrepancies. Thus, the highest stage of automated information processing achieved was information analysis, although most studies (92/94, 97.9%) only automated information acquisition steps. Most studies (67/94, 71.3%) used free-text clinical notes from the electronic health record, although a significant proportion (21/94, 22.3%) used images of pills or images of other medication-related items. Studies using text-based data used a variety of machine learning methods (eg, recurrent neural networks, conditional random fields, support vector machines, and transformers), whereas those that leveraged images typically used convolutional neural networks. Most studies (61/94, 64.9%) used publicly available data from benchmarking datasets (eg, n2c2 2022) and were strictly model development studies, with only 1.1% (n=1) being usability studies. CONCLUSIONS: This is the first review to consider the role of AI in the automation of MedRec tasks, offering a basis for prioritizing future development efforts. Current applications of AI to automate MedRec tasks are preliminary, with most work focusing on the extraction of medication information and limited to proof-of-concept model development. Future work should consider addressing infrastructural barriers to the AI-based automation of MedRec tasks (eg, data incompleteness in sources of medication information) and exploring approaches to automate discrepancy resolution. Beyond developing models, there is also a need to implement them in tools and evaluate them in real-world contexts.
13 May 2026
Read appraisal →BMJ supportive & palliative care
Hand-foot syndrome prevention: pharmacological interventions-systematic review and network meta-analysis
INTRODUCTION: Hand-foot syndrome (HFS) is a dermatological side effect of chemotherapies such as capecitabine and pegylated liposomal doxorubicin. Over recent years, numerous randomised controlled trials (RCTs) have investigated various pharmacological strategies to prevent HFS. Although urea cream and celecoxib have shown promising results, the efficacy of topical diclofenac remains unclear. To provide a comprehensive comparison of pharmacological interventions for HFS prevention in cancer patients undergoing chemotherapy, we conducted a network meta-analysis (NMA). METHODS: We systematically searched PubMed, Cochrane and Scopus for relevant RCTs published up to November 2024. The primary outcome was the incidence of moderate to severe HFS (grade 2-3), while secondary outcomes included the occurrence of all-grade HFS (grade≥1) and chemotherapy modifications. Risk ratios (RR) with 95% CIs were estimated for all outcomes using NMA. RESULTS: Thirteen RCTs comprising 1983 patients, most of whom received capecitabine (n=1849) were included, evaluating five pharmacological interventions. Both topical diclofenac (RR 0.26, 95% CI 0.10 to 0.66) and celecoxib (RR 0.46, 95% CI 0.26 to 0.84) significantly reduced the risk of moderate to severe HFS compared with placebo. Notably, topical diclofenac was the only intervention that significantly reduced the incidence of all-grade HFS and the need for chemotherapy modifications. CONCLUSION: Our NMA demonstrates that topical diclofenac provides the most robust protective effect among available pharmacological interventions for HFS prevention and is also associated with fewer chemotherapy modifications. These findings support its consideration as a preferred preventative strategy for patients at risk of HFS during chemotherapy. PROSPERO REGISTRATION NUMBER: CRD42025643635.
3 May 2026
Read appraisal →European journal of hospital pharmacy : science and practice
How to manage the overwhelming amount of database-detected interactions? A focus group meeting study on the management of database-detected risks of drug interactions.
OBJECTIVES: Database-assisted screening may help detect the risks of drug interactions, but less than 10% of flagged interactions have clinical manifestations, potentially leading to alert fatigue among healthcare providers. To address this issue, this study aimed to develop a concise framework to support the management of database-indicated interaction risks. METHODS: A grounded theory approach was used to design, evaluate and develop the framework. Participants included healthcare professionals and laypersons with experience using narrow therapeutic index medications, as well as individuals recommended by them. Two focus group discussions were conducted (N1=6 and N2=5), each lasting approximately 2 hours and following the same agenda. In one case, a follow-up one-on-one interview was held because the participant indicated further insights at the end of the session. RESULTS: Participants identified key decision-making questions for the framework, including the evidence base for interacting drugs, severity and documentation level of the interaction, availability of safer alternatives, and the presence of confounding factors. The framework was considered useful and, following refinement, potentially suitable for clinical implementation. A key insight was that integration into healthcare curricula is essential for achieving long-term impact. Overall, the proposed tool may assist in managing interaction risks in diverse clinical scenarios and reduce alert fatigue among healthcare professionals. CONCLUSION: Although further clinical validation is needed, the framework provides a foundation for improving the management of database-detected drug interactions.
26 Apr 2026
Read appraisal →European journal of hospital pharmacy : science and practice
Algorithm-managed dosing and pharmacist-managed dosing of erythropoietin stimulating agents in renal anaemia: a systematic review
OBJECTIVES: The goal of this systematic review was to identify and summarise algorithm-managed and pharmacist-managed dosing of erythropoietin stimulating agents (ESA) in patients with renal anaemia and to determine the effects on available outcome parameters. METHODS: We followed the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines for systematic reviews. Studies investigating algorithm-managed and pharmacist-managed dosing of ESA in adult patients with renal anaemia were evaluated for inclusion. No restrictions were set on outcome parameters. Observational and interventional studies available as full-text articles with a control group and follow-up ≥6 months were eligible for inclusion. Relevant databases were searched from their inception through August 2024. Two independent reviewers evaluated all studies. The risk of bias was assessed by the ROBINS-I and RoB1 tools. The protocol of this study was registered in PROSPERO (CRD42021243678). RESULTS: After screening 140 articles, 17 articles and 4313 patients could be included. Available evidence was of low to moderate quality with a high risk of bias. Data were summarised and tabulated. Meta-analysis was not possible due to the substantial heterogeneity in participants, study design, interventions, comparisons, and outcome parameters. However, standardised metrics could be identified and calculated for haemoglobin and ESA dose. The percentage in target range for haemoglobin varied between 3.5% lower (95% CI -18.67% to +11.67%) to 32.0% higher (95% CI 14.07% to 49.93%) in the pharmacist-managed group versus the control group (n=1401). The range in reduction in ESA dose was 5.45% (95% CI -7.97% to +18.87%) to 49.97% (95% CI 20.32% to 79.61%) in the pharmacist-managed group versus the control group (n=2115). CONCLUSION: Low-quality data with high risk of bias suggest that pharmacist-managed renal anaemia may improve the percentage of haemoglobin within target range and reduce the ESA dose. However, meta-analysis was impossible due to substantial heterogeneity. Therefore, no definite conclusions could be drawn on the effectiveness of pharmacist-managed dosing of ESA in renal anaemia. PROSPERO REGISTRATION NUMBER: CRD42021243678.
26 Apr 2026
Read appraisal →American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists
Feasibility and acceptability of incorporating telemedicine into pharmacy services in the rheumatology clinics of the School of Medicine of the University of Puerto Rico
PURPOSE: Incorporating telemedicine into pharmacist services can promote medication adherence, reduce polypharmacy, and prevent adverse drug events as part of identification of drug-related problems. Our primary aim was to measure the acceptability to patients of telemedicine services using a standardized patient satisfaction survey and compare it to that for face-to-face services. Our secondary aim was to assess the feasibility of the telemedicine modality for pharmaceutical care interventions by measuring, quantifying, and categorizing drug-related problems identified during the service. METHODS: The study followed a prospective crossover design from October 2021 to September 2022. Adult patients (21 years or older) with rheumatic diseases evaluated at a university-based outpatient clinic were eligible to be enrolled. After the intervention, patients completed a survey regarding satisfaction and perception of their appointment. Descriptive statistics were used for patient demographic characteristics. Patient satisfaction with the modalities was compared using a t test. RESULTS: In this study, 296 patients were enrolled with a mean (SD) age of 50.4 (12.5) years. Participating patients were utilizing a mean (SD) of 10.2 (4.7) medications. No significant differences (P < 0.05) were observed for any of the survey questions when comparing responses for the face-to-face and telemedicine encounters. In general, patients strongly agreed that they were satisfied with the services they received and perceived the services as unique and valuable in both types of encounters. CONCLUSION: Overall satisfaction was very high, and no differences were found in the perception of or satisfaction with services between the telemedicine and face-to-face modalities.
24 Apr 2026
Read appraisal →International journal of pharmaceutics
Beyond spectroscopy: Machine vision as the future of non-destructive testing in 3D-printed pharmaceuticals
3D Printing (3DP) has revolutionized personalized medicine by enabling the production of patient-specific dosage forms and adaptable drug delivery systems. However, despite these advances, 3DP presents significant challenges in quality control (QC), particularly as traditional methods may not be suitable for small-scale or on-demand manufacturing. To address these challenges, it is essential to develop flexible, precise, and efficient QC strategies that ensure product integrity. This review focuses on non-destructive spectroscopic- and imaging-based testing techniques that can bridge this gap. Techniques such as Near-Infrared Spectroscopy, Raman Spectroscopy, Terahertz Spectroscopy, Optical Coherence Tomography, and Hyperspectral Imaging provide robust alternatives to conventional QC approaches. Moreover, Machine Vision (MV) is emerging as a promising tool for real-time defect detection and visual inspection. When integrated with advanced algorithms and artificial intelligence, MV enables automated decision-making and defect identification in 3DP pharmaceuticals. Recent advancements in deep learning, especially through Convolutional Neural Networks (CNNs) and You Only Look Once (YOLO)-based models, have further enhanced the potential of MV in this domain, offering improved accuracy, speed, and adaptability under varying conditions. This review also includes a comparative evaluation of these techniques, highlighting the increasing importance of non-destructive tools, particularly MV, in supporting innovation and ensuring quality within an increasingly personalized pharmaceutical landscape. Furthermore, it also discusses existing gaps, regulatory challenges, and the need for standardization to ensure the safe and reliable integration of these technologies into pharmaceutical manufacturing workflows.
10 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Real-world incidence of nausea and vomiting induced by antineoplastic agents: insights and future directions from the Japanese adverse drug event report database
PURPOSE: Antineoplastic treatment commonly leads to nausea and vomiting (NV), which considerably affects the quality of life and adherence to therapy of patients. This study aimed to analyze the incidence of NV induced by antineoplastic agents using the Japanese Adverse Drug Event Report (JADER) database and to explore novel antiemetic therapies for oral antineoplastic agents. METHODS: Data from April 2004 to March 2023 were extracted from the JADER database. The analysis considered guideline-recommended antiemetics and involved the temporal evaluation of cases with NV. The onset patterns of NV were analyzed for each oral antineoplastic agent. The reported odds ratio was used to identify adjunctive medications that showed inverse associations with these symptoms. RESULTS: A large number of NV cases were reported for platinum compounds and irinotecan. Among the oral agents, a notable incidence of NV was induced by antimetabolites and tyrosine kinase inhibitors. A decreasing trend in the use of single-agent antiemetic therapy was observed from 2010. Distinct NV patterns were identified. Silodosin and meropenem emerged as potential adjunct therapies to suppress NV induced by oral agents. CONCLUSION: Although there is a trend toward improvement in NV management with increased adherence to antiemetic therapies, a substantial gap remains between guidelines and actual practice. The need for tailored management strategies based on the onset patterns of NV for specific agents has been emphasized. Investigating the mechanisms underlying the antiemetic effects of adjunctive therapies, such as silodosin and meropenem, may pave the way for new antiemetic options for oral antineoplastic agents.
9 Apr 2026
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