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Response to "Shot in the Thigh or Stab in the Dark? Challenging the Evidence for IM Adrenaline in OHCA".
Resuscitation
March 2025
Department of Emergency Medicine University of Utah, Salt Lake City, Utah, USA; Salt Lake City Fire Department Salt Lake City, Utah, USA.
Boosting the Pixel Acquisition Rate of Elemental Mapping with Laser Ablation-ICP-Mass Spectrometry to 1000 Hz.
Anal Chem
March 2025
Department of Chemistry, Atomic and Mass Spectrometry - A&MS Research Group, Ghent University, Campus Sterre, Krijgslaan 281-S12, Ghent 9000, Belgium.
Novel low-dispersion ablation cell designs and highly efficient aerosol transport systems have enabled fast elemental mapping using laser ablation-ICP-mass spectrometry (LA-ICP-MS) at high spatial resolution and its application in various research fields. Nowadays, the fastest low-dispersion setups enable narrow single pulse responses (SPR, duration of the transient signal observed upon a single laser shot), which enhance the signal-to-noise ratio and boost the pixel acquisition rate attainable in elemental mapping applications. In this work, the analytical performance of a nanosecond 193 nm ArF* excimer-based kHz laser in combination with a low-dispersion tube-type ablation cell, coupled to an ICP-mass spectrometer equipped with a time-of-flight (ToF) analyzer, was evaluated.
View Article and Find Full Text PDFBackground: Large language models (LLMs) are increasingly used in the medical field for diverse applications including differential diagnostic support. The estimated training data used to create LLMs such as the Generative Pretrained Transformer (GPT) predominantly consist of English-language texts, but LLMs could be used across the globe to support diagnostics if language barriers could be overcome. Initial pilot studies on the utility of LLMs for differential diagnosis in languages other than English have shown promise, but a large-scale assessment on the relative performance of these models in a variety of European and non-European languages on a comprehensive corpus of challenging rare-disease cases is lacking.
View Article and Find Full Text PDFLarge language models for analyzing open text in global health surveys: why children are not accessing vaccine services in the Democratic Republic of the Congo.
Int Health
March 2025
Institute for Disease Modeling, Gates Foundation, Seattle, Washington 98109 USA.
Background: This study evaluates the use of large language models (LLMs) to analyze free-text responses from large-scale global health surveys, using data from the Enquête de Couverture Vaccinale (ECV) household coverage surveys from 2020, 2021, 2022 and 2023 as a case study.
Methods: We tested several LLM approaches consisting of zero-shot and few-shot prompting, fine-tuning, and a natural language processing approach using semantic embeddings, to analyze responses on the reasons caregivers did not vaccinate their children.
Results: Performance ranged from 61.
WP-FSCIL: A Well-Prepared Few-shot Class-incremental Learning Framework for Pill Recognition.
IEEE J Biomed Health Inform
March 2025
Few-shot Class-incremental Pill Recognition (FSCIPR) aims to develop an automatic pill recognition system that requires only a few training data and can continuously adapt to new classes, providing technical support for applications in hospitals, portable apps, and assistance for visually impaired individuals. This task faces three core challenges: overfitting, fine-grained classification problems, and catastrophic forgetting. We propose the Well-Prepared Few-shot Class-incremental Learning (WP-FSCIL) framework, which addresses overfitting through a parameter-freezing strategy, enhances the robustness and discriminative power of backbone features with Center-Triplet (CT) loss and supervised contrastive loss for fine-grained classification, and alleviates catastrophic forgetting using a multi-dimensional Knowledge Distillation (KD) strategy based on flexible Pseudo-feature Synthesis (PFS).
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