High Yield Medical Reviews

From Scene to Survival: Anesthetic and Critical Care Insights into Out-of-Hospital Cardiac Arrest Outcomes

Mariam Nofal — 2025-12-01

Out-of-hospital cardiac arrest (OHCA) occurs when the heart pauses to function outside of a medical facility. The high mortality rate persists despite advancements in resuscitation research, rendering it a significant global public health issue. To substantially reduce mortality associated with OHCA, a comprehensive understanding of all management phases—pre-hospital, in-hospital, and post-discharge—is essential. Pre-hospital factors, such as community socioeconomic level, bystander cardiopulmonary resuscitation (CPR), and access to defibrillators, are essential for early survival. In-hospital variables, like the accessibility of round-the-clock cardiac interventional treatments and structured emergency reception systems, can affect outcomes. Post-discharge survival mostly depends on patient adherence to medical and lifestyle interventions, psychological support, and rehabilitation programs. This assessment consolidates information about the factors influencing the management of OHCA and identifies critical issues and opportunities within the Jordanian healthcare system.

Evaluating the Quality of Systematic Reviews: A Narrative Review of Current Appraisal Frameworks and Introduction of the High Yield Med Tool

Nouran Alwisi — 2025-12-01

AI-assisted workflows are transforming the way systematic reviews are conducted, converting complex evidence synthesis processes into rapid, high-throughput outputs. This shift significantly reduces the time required for evidence synthesis, making systematic reviews more scalable and accessible. A critical comparison of existing appraisal tools including AMSTAR/AMSTAR 2, ROBIS, JBI, CASP, MECIR, and GRADE highlights that these frameworks focus primarily on transparent reporting and retrospective methodological quality, failing to capture the integrity of the review process, measure reproducibility, or adequately assess automation checkpoints inherent in modern hybrid workflows. To address this gap and support critical appraisal, we introduce the High Yield Med Quality Evaluation Tool (HYMQET), a novel framework designed to provide a structured, quantitative assessment of workflow quality, methodological rigor, and automation transparency in both human-led and hybrid human-AI systematic reviews. The HYMQET employs a stepwise, workflow-based scoring system across five core domains: Query Development, Screening Quality, Field Selection for Data Extraction, Full-Text Data Extraction, and Manuscript Writing. Its quantitative, workflow-based structure makes it an essential tool for the external validation, quality control, and reliable benchmarking of emerging automated and hybrid systematic review methodologies.

Systematic Review of Reporting guidelines for large language models used in healthcare research

Saif Aldeen Alryalat — 2025-12-01

This systematic review aims to synthesize existing reporting guidelines for large language models (LLMs) in healthcare research and evaluate their adequacy in addressing gaps in transparency, reproducibility, and clinical applicability. A systematic search was conducted to identify relevant studies on reporting guidelines for LLMs used in healthcare research using the PubMed database. We included 18 studies focused on reporting guidelines for LLMs used in healthcare research. The studies primarily aimed to develop or evaluate reporting frameworks to improve transparency, reproducibility, and methodological rigor in LLM applications. Several studies focused on creating structured reporting checklists for LLM applications in healthcare. The Chatbot Assessment Reporting Tool (CHART) was developed across multiple studies. Similarly, TRIPOD-LLM extended the TRIPOD+AI framework with 19 main items and 50 subitems, emphasizing modular reporting for diverse LLM tasks. Ultimately, while existing reporting guidelines represent an important advancement toward standardizing LLM research, their long-term impact will rely on broad adoption and iterative refinement to meet the evolving challenges of artificial intelligence.

Drivers of Influence in Top Transplant Journals: Predictors of Positive Impact

Badi Rawashdeh — 2025-12-01

Introduction: The impact of scientific articles is often measured using metrics such as the Journal Impact Factor (JIF) and CiteScore, which rely on citation counts and publication volume. In this study, we aimed to identify the predictors of article impact in the leading solid organ transplantation journals from 2019 to 2020. Article impact was determined using its citation count in 2021 compared to the JIF.

Methods: Statistical analysis was performed using IBM SPSS Statistics to identify significant predictors of positive impact on the JIF. A total of 2,461 articles and reviews were included, with variations observed among the top transplantation journals.

Results: Articles discussing kidney transplantation and COVID-19 were associated with a positive impact on the JIF, while those discussing lung transplantation had a negative impact. Open access publishing also correlated with increased citations and a positive impact on the JIF.

Conclusion: Further research incorporating a broader range of journals and years would provide a more comprehensive understanding of factors influencing article impact.

Artificial Intelligence for scientific research and discoveries

Saif Aldeen Alryalat — 2025-12-01

The integration of artificial intelligence (AI) into scientific research is accelerating rapidly, with large language models (LLMs) and generative AI tools now widely adopted by researchers worldwide. While the most common use of LLMs is primarily to enhance written outputs, emerging uses like streamlining systematic reviews and improving research efficiency, with some AI-assisted workflows demonstrating over 350-fold acceleration while maintaining expert-level quality. Beyond text, AI is increasingly applied in drug and protein target discovery, enabling rapid identification of previously inaccessible targets and accelerating early-stage drug development. In medical imaging, multimodal AI models have shown the ability to detect pathologies such as glaucoma from fundus photographs with high accuracy, and generative models can create high-quality, medical-grade images to augment datasets, aid training, and produce educational figures. Despite potential risks, the benefits of these technologies are becoming increasingly evident, with AI poised to transform research methodology, diagnostics, and medical education.