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Related Concept Videos

Improving Translational Accuracy02:07

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...
Improving Translational Accuracy02:07

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...

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Related Experiment Video

Updated: Jun 29, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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

Published on: December 6, 2024

Chain-of-verification prompting for NIH stroke scale extraction using small and frontier large language models.

Jack Lott1, Brett Stubbert2, David McShannon3

  • 1School of Medicine, Queen's University, 80 Barrie Street, Kingston, ON K7L 3N6, Canada.

International Journal of Medical Informatics
|June 27, 2026
PubMed
Summary

Chain-of-Verification (CoVe) prompting significantly enhances the extraction of National Institutes of Health Stroke Scale (NIHSS) data by smaller large language models (LLMs). While performance in larger models remains unchanged, CoVe shows promise for improving automated stroke care documentation.

Keywords:
Chain-of-verificationClinical information extractionLarge language modelsNIH stroke scaleNatural language processing

Related Experiment Videos

Last Updated: Jun 29, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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

Published on: December 6, 2024

Area of Science:

  • Natural Language Processing
  • Artificial Intelligence in Medicine
  • Clinical Informatics

Background:

  • The National Institutes of Health Stroke Scale (NIHSS) is crucial for acute stroke care but is often embedded in unstructured clinical notes.
  • Automated extraction of NIHSS data using large language models (LLMs) is desirable, but smaller models typically underperform compared to advanced systems.
  • Chain-of-Verification (CoVe) prompting is a novel technique designed to improve LLM performance through structured self-verification.

Purpose of the Study:

  • To evaluate the impact of CoVe prompting on the performance of various LLMs for extracting NIHSS data from clinical notes.
  • To compare the effectiveness of CoVe across different sizes of LLMs, specifically small models versus frontier models.

Main Methods:

  • Eight LLMs, including four small models (LLaMA 3.2 3B, Mistral 3B, Gemma 3 4B, Qwen 3 4B) and four frontier models (GPT-5.2, Gemini 3 Pro, Claude Opus 4.5, Grok 4), were assessed.
  • The models processed 312 patient discharge summaries, evaluated under both baseline and CoVe prompting conditions.
  • Performance metrics included subscore and total score exact-match accuracy and mean absolute error (MAE).

Main Results:

  • At baseline, frontier models significantly outperformed small models in subscore accuracy (88.5% vs 53.2%) and MAE (0.15 vs 0.84).
  • CoVe prompting markedly improved small model performance, increasing subscore accuracy to 65.0% and reducing subscore MAE to 0.55.
  • Frontier models demonstrated no significant performance improvement with CoVe prompting at the group level.

Conclusions:

  • CoVe prompting substantially enhances the accuracy of NIHSS extraction by smaller LLMs.
  • While CoVe does not significantly benefit frontier models, it presents a viable strategy for improving the utility of smaller LLMs in clinical data extraction.
  • Current performance of even improved small models may not suffice for standalone clinical use, but CoVe offers a path for future development.