ArticleStroke2025
Accuracy of Large Language Models to Identify Stroke Subtypes Within Unstructured Electronic Health Record Data.
Article in Stroke, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- The Performance of Large Language Models in Extracting Intestinal Symptoms From Electronic Health Records: Retrospective Observational Study.Journal of medical Internet research · 2026Observational
- Multilingual Evidence-Based Question-Answering for Stroke Discharge Summaries: Study of Cross-Lingual Heterogeneity in Clinical Reports.Journal of medical Internet research · 2026Article
- A Multiagent Large Language Model Framework for Emergency Treatment Recommendation in Acute Ischemic Stroke: Development and Validation Study.Journal of medical Internet research · 2026Article
- Adaptive Fast-Slow Large Language Model Framework for Multidimensional Classification of Prenatal Ultrasound Reports: Comparative Study.Journal of medical Internet research · 2026Article
- Malignant cerebral edema after endovascular thrombectomy: a multimodal prediction model based on post-thrombectomy cerebral hyperdensity and natural language processing.Frontiers in medicine · 2026Article
- Performance and usability of retrieval-augmented large language models for stroke patient and caregiver support.Digital healthArticle
- A multimodal deep learning model for predicting early neurological deterioration in patients with acute ischemic stroke.Frontiers in neurologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundWhile
methodsWe implemented a retrieval-augmented generation framework with GPT-4o to classify stroke types (ischemic versus hemorrhagic) and ischemic stroke subtypes using electronic health records data. The American Heart Association Get With The Guidelines-Stroke registry served as the gold standard. Model development used a 20% subset of Get With The Guidelines-Stroke-linked data from UT Southwestern Medical Center (UTSW), with the remaining 80% reserved for testing. External validation used data from the Parkland Health and Hospital System (PHHS). A total of 4123 stroke hospitalizations from January 2019 to August 2023 were included (UTSW: n=2047; PHHS: n=2076). Three prompting strategies-zero-shot chain-of-thought, expert-guided, and instruction-based-were evaluated. Predictions of GPT-4os were compared with classifications made by trained abstractors contributing to the Get With The Guidelines-Stroke registry.
resultsIn the external validation set, 79.6% of patients had ischemic stroke and 20.4% hemorrhagic. GPT-4o achieved 98% accuracy (95% CI, 0.97-0.99) in classifying stroke type, where accuracy reflects the overall proportion of correctly classified patients. Sensitivity was 0.98 (95% CI, 0.97-0.99), and specificity was 0.97 (95% CI, 0.96-0.98). For ischemic stroke subtypes, sensitivity ranged from 0.40 (95% CI, 0.31-0.49) for cryptogenic to 0.95 (95% CI, 0.93-0.97) for small-vessel occlusion. Specificity ranged from 0.94 (95% CI, 0.92-0.96) for large-artery atherosclerosis to 0.98 (95% CI, 0.97-0.99) for cardioembolism. Zero-shot chain-of-thought prompting-requiring minimal human input-performed comparably to more labor-intensive strategies. Consistency analysis revealed
conclusionsGPT-4o demonstrated strong accuracy in classifying stroke types but faced challenges with ischemic subtypes.
Indexed as
Identifiers
What Socratic 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 Socratic graph.