ArticleJournal of pain and symptom management2026
Assessment of a Zero-Shot Large Language Model in Measuring Documented Goals-of-Care Discussions.
Article in Journal of pain and symptom management, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed.
- Review
- Large Language Models for Summarizing Advance Care Planning Information From Goals of Care Notes in the EHR.Learning health systems · 2026Article
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7 authors.
Funding
Abstract
contextGoals-of-care (GOC) discussions and their documentation are important process measures in palliative care. However, existing natural language processing (NLP) models for identifying such documentation require costly task-specific training data. Large language models (LLMs) hold promise for measuring such constructs with fewer or no task-specific training data.
objectiveTo evaluate the performance of a publicly available LLM with no task-specific training data (zero-shot prompting) for identifying documented GOC discussions.
methodsWe compared performance of two NLP models in identifying documented GOC discussions: Llama 3.3 using zero-shot prompting; and, a task-specific BERT (Bidirectional Encoder Representations from Transformers)-based model trained on 4,642 manually annotated notes. We tested both models on records from a series of clinical trials enrolling adult patients with chronic life-limiting illness hospitalized over 2018-2023. We evaluated the area under the receiver operating characteristic curve (AUC), area under the precision-recall curve (AUPRC), and maximal F
resultsIn our text corpora, GOC documentation represented <1% of text and was found in 7.3-9.9% of notes for 23-37% of patients. In a 617-patient held-out test set, Llama 3.3 (zero-shot) and BERT (task-specific, trained) exhibited comparable performance in identifying GOC documentation (Llama 3.3: AUC 0.979, AUPRC 0.873, and F
conclusionA zero-shot large language model with no task-specific training performed similarly to a task-specific trained BERT model in identifying documented goals-of-care discussions. This demonstrates the promise of LLMs in measuring novel clinical research outcomes.
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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.