Researchers conducted a systematic review to assess the risk of bias and applicability of prediction models for fear of recurrence in patients with cancer.
Researchers have unveiled an interpretable, lightweight AI text detection framework using classical machine learning models that achieves near-perfect accuracy while lowering computational costs.
Why This MattersChildren undergoing thoracic surgery face unique challenges, especially when one-lung ventilation (OLV) is ...
Background Native aortic valve endocarditis continues to present significant operative challenges, often complicated by heart ...
New research using AI-powered stacked ensemble models has improved accuracy in predicting NBA game results by combining multiple machine learning algorithms. These models not only forecast outcomes ...
Introduction Postpartum disengagement from HIV care increases risks for adverse maternal health outcomes and transmission of ...
THE VIBRANT city of London, UK, set the stage for Europe’s leading urological gathering this year: the 41st Annual European ...
New research suggests that weight gained in the first 12 weeks of antipsychotic treatment is the biggest driver of long-term ...
Using single-nucleus RNA sequencing, the authors map transcriptional changes in the rat ventral tegmental area following chronic inflammatory pain and acute morphine exposure. Notably, their ...
Objectives To assess physical and mental symptoms by ethnicity of a UK Armed Forces cohort. Design A retrospective, pooled ...
The offline pipeline's primary objective is regression testing — identifying failures, drift, and latency before production.
Background:The COVID-19 pandemic affected the epidemiology of respiratory syncytial virus (RSV). We sought to describe ...
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