ArticleNPJ digital medicine2025
The foundational capabilities of large language models in predicting postoperative risks using clinical notes.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Precision Medicine and Artificial Intelligence in Next-Generation Cancer Surgery: A Comprehensive Analysis of Clinical Applications, Therapeutic Outcomes, and Implementation Strategies.Asian Pacific journal of cancer prevention : APJCP · 2025Pooled it
- Applying Large Language Models in Perioperative Medicine With an Equity-Informed Perspective.Anesthesia and analgesia · 2026Article
- The Anesthesia Preparedeness Clinic (APC) triage score tool: an integrated electronic health record tool for improving resource allocation for preoperative care clinics.Journal of clinical monitoring and computing · 2026Article
- Large language model driven multicenter prediction and explainable risk attribution of acute kidney injury.Nature communications · 2026Article
- Review
- Perioperative Risk Stratification with AI-Powered Chatbots: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2026Review
- Supervised Fine-Tuning of Large Language Models With Chain-of-Thought Reasoning for Pediatric Heart Disease Detection in Unstructured Echocardiogram Reports: Algorithm Development and Validation.JMIR formative research · 2026Article
- Domain-Aware Interpretable Machine Learning Model for Predicting Postoperative Hospital Length of Stay from Perioperative Data: A Retrospective Observational Cohort Study.Bioengineering (Basel, Switzerland) · 2026Article
- Clinically-guided models or foundation models? predicting cervical spondylotic myelopathy from electronic health records.NPJ digital medicine · 2026Article
- Beyond static risk scores: dynamic world models simulating patient-specific trajectories to inform preoperative risk mitigation strategies.Frontiers in medicine · 2026Article
- Artificial intelligence for perioperative precision in surgical oncology.Frontiers in surgery · 2026Review
- Traditional Cox regression outperforms large language models in predicting long-term progression of intermediate to advanced hepatocellular carcinoma.Frontiers in oncology · 2026Article
- A multimodal generative model for structured and unstructured electronic health records.npj health systems · 2026Article
- Improving postoperative length of stay forecasting with retrieval-augmented prediction.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- The complex task of modelling artificial intelligence workflows for forecasting postoperative risk.Journal of anesthesia, analgesia and critical care · 2025Article
- How Well Does ChatGPT-4o Reason? Expert Evaluation of Diagnostic and Therapeutic Performance in Hand Surgery.Journal of clinical medicine · 2025Article
- Association between stress hyperglycemia ratio and all-cause mortality in patients with acute kidney injury: a retrospective analysis of the MIMIC-IV database using machine learning.BMC nephrology · 2025Article
- HONeYBEE: enabling scalable multimodal AI in oncology through foundation model-driven embeddings.NPJ digital medicine · 2025Article
- GenAI exceeds clinical experts in predicting acute kidney injury following paediatric cardiopulmonary bypass.Scientific reports · 2025Article
- The Role of Large Language Models (LLMs) in Hepato-Pancreato-Biliary Surgery: Opportunities and Challenges.Cureus · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Clinical notes recorded during a patient's perioperative journey holds immense informational value. Advances in large language models (LLMs) offer opportunities for bridging this gap. Using 84,875 preoperative notes and its associated surgical cases from 2018 to 2021, we examine the performance of LLMs in predicting six postoperative risks using various fine-tuning strategies. Pretrained LLMs outperformed traditional word embeddings by an absolute AUROC of 38.3% and AUPRC of 33.2%. Self-supervised fine-tuning further improved performance by 3.2% and 1.5%. Incorporating labels into training further increased AUROC by 1.8% and AUPRC by 2%. The highest performance was achieved with a unified foundation model, with improvements of 3.6% for AUROC and 2.6% for AUPRC compared to self-supervision, highlighting the foundational capabilities of LLMs in predicting postoperative risks, which could be potentially beneficial when deployed for perioperative care.
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.