ClinicalTrials.gov
public full text
Public Domain
Evaluation of ChatGPT-5 and Anesthesiologist Predictions of Postoperative Intensive Care Unit Admission Using Preoperative Clinical Data
Accurate prediction of postoperative intensive care unit (ICU) requirement is essential for patient safety and efficient use of limited ICU resources. In routine clinical practice, decisions regarding postoperative ICU admission are primarily based on anesthesiologists' preoperative clinical judgment, which may vary among clinicians. This prospective, observational study aims to evaluate the agreement between predictions made by ChatGPT-5(Chat Generative Pre-trained Transformer) and anesthesiologists regarding postoperative ICU requirement using routinely collected preoperative patient data, and to compare these predictions with actual postoperative ICU admission outcomes. The study does not involve any intervention, treatment modification, or additional procedures beyond standard clinical care. All patient data are anonymized, and clinical management is not influenced by the model's predictions
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Success of ChatGPT in Determining the Need for Postoperative Intensive Care#981855
- Delphi Septic Shock: Sub-phenotypes Using Point-of-Care Ultrasound in Patients With Septic Shock#885876
- Machine learning-based prediction of intraoperative blood transfusion in major surgery: exploiting clinical variables and systemic inflammatory indices.#450207
- Machine learning-based prediction of intraoperative blood transfusion in major surgery: exploiting clinical variables and systemic inflammatory indices.#449837
- Early Prediction of Postoperative Urinary Retention After General Anesthesia Using Explainable Machine Learning.#1107495
Record · ID 993715
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive