ArticleNPJ digital medicine2025
Developing a named entity framework for thyroid cancer staging and risk level classification using large language models.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07234539 (A Randomized Controlled Trial to Evaluate an Artificial Intelligence-enabled Clinical Assistant Leveraging Large Language Models for Thyroid Cancer Staging and Risk Stratification Among Medical Students and Clinicians), which is not on this map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Randomized Controlled Trial to Evaluate an Artificial Intelligence-enabled Clinical Assistant Leveraging Large Language Models for Thyroid Cancer Staging and Risk Stratification Among Medical Students and Clinicians
Who cites it
10 citing papers in PubMed.
- Review
- A workflow utilizing general-purpose large language models for efficient structuring and data mining of bone scintigraphy narratives.Scientific reports · 2026Article
- From Pixels to Prediction: Developing Integrated AI Foundation Models for Personalized Thyroid Cancer Care.Cancers · 2026Review
- Evaluating Chain-of-Thought reasoning in large language models for thyroid ultrasound interpretation: a dual-information approach.Frontiers in artificial intelligence · 2026Article
- Precision oncology: from large language models to multi-agent systems.Frontiers in oncology · 2026Review
- Navigating the complexity of WHO CNS5: the evolutionary trajectory of glioma classification and the emergence of large language models.Frontiers in oncology · 2026Review
- Traditional Cox regression outperforms large language models in predicting long-term progression of intermediate to advanced hepatocellular carcinoma.Frontiers in oncology · 2026Article
- Combining Clinician Expertise with Prompt Engineering enhances Small Language Models Reliability for Cancer Entity Recognition in Electronic Health Records.medRxiv : the preprint server for health sciences · 2025Article
- Developing a thyroid cancer differentiation state classification system using deep residual networks and metabolic signature profiling.NPJ digital medicine · 2025Article
- The potential of large language models to advance precision oncology.EBioMedicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors.
Funding
Abstract
We developed a named entity (NE) framework for information extraction from semi-structured clinical notes retrieved from The Cancer Genome Atlas-Thyroid Cancer (TCGA-THCA) database and examined Large Language Models (LLMs) strategies to classify the 8
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.