Search research articles

Related Concept Videos

Improving Translational Accuracy

Improving Translational Accuracy

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Historical redlining and neighborhood deprivation are associated with caregiver exclusion following evaluation for suspected child physical abuse: A multicenter study.

The journal of trauma and acute care surgery·2026

Same author

Evaluation of thoracolumbar spine injury utilizing plain film and physical exam in the pediatric population: A multicenter study.

The journal of trauma and acute care surgery·2026

Same author

Surgeons' perceptions of artificial intelligence (AI) for gaze guidance in laparoscopic cholecystectomy.

Surgical endoscopy·2026

Same author

Describing What Surgeons See: Visual Orientation Strategies in Laparoscopic Cholecystectomy Across Imaging Conditions.

Journal of surgical education·2026

Same author

Assessing quality of life in individuals with hereditary cancer risk: Results from phases 1-3a of the EORTC QLQ-HCR30 questionnaire.

European journal of cancer (Oxford, England : 1990)·2026

Same author

snoCLASH Reveals Extensive snoRNA-mRNA Interaction Networks.

bioRxiv : the preprint server for biology·2025

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Feb 28, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Improving Trauma Triage Accuracy with Large Language Models: A Comparison to Human Expert Decisions.

Ascharya Kushidhan Balaji¹, Brendan T Fox¹, Philip Seger¹

¹From the Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY (Balaji, Fox, Seger, Gorugantu, Nordin, Schwaitzberg, Kim).

Journal of the American College of Surgeons

|February 27, 2026

Summary

This summary is machine-generated.

Large language models (LLMs) show promise in improving pediatric trauma triage accuracy. While retrospective review showed comparable performance to human clinicians, further validation is needed for clinical outcomes.

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Related Experiment Videos

Last Updated: Feb 28, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Trauma Surgery

Background:

Accurate prehospital trauma triage is critical for patient outcomes and healthcare system efficiency.
Large language models (LLMs) present a novel opportunity to enhance prehospital trauma triage processes.
Current implementation of LLMs in prehospital trauma care is limited.

Purpose of the Study:

To evaluate the performance of LLMs in pediatric trauma triage.
To assess the accuracy of LLM-assisted prehospital tele-communication.
To compare LLM triage accuracy against human clinician performance.

Main Methods:

Retrospective cohort study of 133 pediatric trauma activations at a Level I center.
Analysis of EMS recordings, trauma pages, and Injury Severity Scores (ISS).
Utilized OpenAI Whisper for transcription and Named Entity Recognition (NER) for structured data extraction; prospective evaluation involved trauma surgeons.

Main Results:

LLM triage demonstrated comparable accuracy to human clinicians in retrospective analysis (83.5% vs. 78.9%).
LLM-assisted triage showed a trend towards reduced under-triage (4.8% vs. 5.1%) and significantly reduced over-triage (58.6% vs. 71.8%).
Prospective evaluation indicated improved human triage accuracy after LLM exposure, enhancing correct triage decision odds.

Conclusions:

LLMs achieve comparable accuracy to trauma staff in retrospective pediatric trauma triage.
The use of structured "Essential Transcripts" significantly reduced data length while maintaining accuracy.
Further research is essential to validate LLM generalizability, clinical outcomes, and user acceptance for widespread deployment.