ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2025
Identifying Opioid Overdose and Opioid Use Disorder and Related Information from Clinical Narratives Using Large Language Models.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Computable Structured Phenotype Versus Large Language Model Identification of Opioid Use Disorder Using Electronic Health Record Data.Annals of emergency medicine · 2026Article
- Comparing computable structured phenotype- versus large language model-identification of opioid use disorder using electronic health record data.medRxiv : the preprint server for health sciences · 2026Article
- Artificial Intelligence for Opioid Safety Surveillance from Clinical Text: A Clinically Focused Review.Journal of clinical medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Opioid overdose and opioid use disorder (OUD) remain a growing public health issue in the United States, affecting 6.1 million individuals in 2022, more than doubling the 2.5 million from 2021. Accurately identifying the opioid overdose and OUD related information is critical to study the outcomes and develop interventions. This study aims to identify opioid overdose and OUD mentions and their related information from clinical narratives. We compared encoder-based large language models (LLMs) and decoder-based generative LLMs in extracting nine crucial concepts related with opioid overdose and OUD including problematic opioid use. Through a cost-effective p-tuning algorithm, our decoder-based generative LLM, GatorTronGPT, achieved the best strict/lenient F1-score of 0.8637, and 0.9057, demonstrating the efficient of using generative LLMs for opioid overdose/OUD related information extraction. This study provided a tool to systematically extract opioid overdose, OUD, and their related information to facilitate opioid-related studies using clinical narratives.
Identifiers
40502234PMC12150707What 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.