ArticleNPJ digital medicine2024
Comparison of NLP machine learning models with human physicians for ASA Physical Status classification.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07696221 (Comparison of Clinical Assessment and Large Language Models in Preoperative Risk Classification), which is not on this map. Cited by 8 papers.
What it found
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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.
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.
Comparison of Clinical Assessment and Large Language Models in Preoperative Risk Classification: A Retrospective Analysis of ChatGPT, DeepSeek, Gemini, and Claude in ASA Physical Status Classification
Who cites it
8 citing papers in PubMed.
- Evaluation of Small and Large Language Models for Calculation of the ASA Score and Charlson Comorbidity Index in Orthopedic Surgical Patients: A Retrospective Concordance Analysis.Bioengineering (Basel, Switzerland) · 2026Article
- Comparison of large language models and conventional machine learning in postoperative outcome prediction: a retrospective, multi-national development and validation study.Korean journal of anesthesiology · 2026Article
- Artificial Intelligence for American Society of Anesthesiologists Physical Status Classification: Agreement with Clinician Consensus and Temporal Stability Analysis.Journal of clinical medicine · 2026Article
- Comparative Analysis of Large Language Models and Machine Learning for ASA Classification Using Structured Electronic Health Record Data.Journal of medical systems · 2026Article
- Classifying American Society of Anesthesiologists Physical Status With a Low-Rank-Adapted Large Language Model: Development and Validation Study.Journal of medical Internet research · 2026Article
- Artificial intelligence in anesthesiology education: transformative applications, challenges, and future perspectives.Frontiers in medicine · 2026Review
- Efficacy of large language models in detecting postoperative delirium from unstructured clinical notes: A retrospective cohort study.NPJ digital medicine · 2025Article
- Structuring and centralizing breast cancer real-world biomarker data from pathology reports through C-LABDigital healthArticle
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
The American Society of Anesthesiologist's Physical Status (ASA-PS) classification system assesses comorbidities before sedation and analgesia, but inconsistencies among raters have hindered its objective use. This study aimed to develop natural language processing (NLP) models to classify ASA-PS using pre-anesthesia evaluation summaries, comparing their performance to human physicians. Data from 717,389 surgical cases in a tertiary hospital (October 2004-May 2023) was split into training, tuning, and test datasets. Board-certified anesthesiologists created reference labels for tuning and test datasets. The NLP models, including ClinicalBigBird, BioClinicalBERT, and Generative Pretrained Transformer 4, were validated against anesthesiologists. The ClinicalBigBird model achieved an area under the receiver operating characteristic curve of 0.915. It outperformed board-certified anesthesiologists with a specificity of 0.901 vs. 0.897, precision of 0.732 vs. 0.715, and F1-score of 0.716 vs. 0.713 (all p <0.01). This approach will facilitate automatic and objective ASA-PS classification, thereby streamlining the clinical workflow.
Identifiers
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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.