Association and Prognostic Implications of "No-Reflow Phenomenon" and Hypercoagulability in Patients With ST-Segment Elevation Myocardial Infarction.

Background: Following percutaneous coronary intervention (PCI), the "no-reflow phenomenon" is associated with a worse outcome. However, it remains unclear how to prevent and treat this phenomenon during PCI.

Objectives: This study aimed to evaluate the association between thrombogenicity profiles and "no-reflow phenomenon" during primary PCI in patients with ST-segment elevation myocardial infarction (STEMI).

AI Agents in Clinical Medicine: A Systematic Review.

Background: AI agents built on large language models (LLMs) can plan tasks, use external tools, and coordinate with other agents. Unlike standard LLMs, agents can execute multi-step processes, access real-time clinical information, and integrate multiple data sources. There has been interest in using such agents for clinical and administrative tasks, however, there is limited knowledge on their performance and whether multi-agent systems function better than a single agent for healthcare tasks.

A machine learning algorithm to predict the success of a second microsurgical testicular sperm extraction.

Introduction: Testicular sperm extraction (TESE) is a common procedure for retrieving sperm in men with azoospermia. However, the success rates of a second TESE following an initial unsuccessful attempt remain low. This study aims to develop and evaluate a machine learning algorithm to predict the success of a second microsurgical TESE (microTESE).

Feasibility of acquiring occupational data from cancer patients' medical records in Malaysian oncology centres.

Background: In Malaysia, data on occupational cancer are severely lacking in medical records across the healthcare system. Incomplete or missing information on occupational background, stemming from non-mandatory reporting in routine patient history taking, has led to the underreporting of cases. This study aimed to evaluate the availability of occupational data in the medical records of cancer patients.

Orchestrated multi agents sustain accuracy under clinical-scale workloads compared to a single agent.

We tested state-of-the-art large language models (LLMs) in two configurations for clinical-scale workloads: a single agent handling heterogeneous tasks versus an orchestrated multi-agent system assigning each task to a dedicated worker. Across retrieval, extraction, and dosing calculations, we varied batch sizes from 5 to 80 to simulate clinical traffic. Multi-agent runs maintained high accuracy under load (pooled accuracy 90.