A random forest algorithm-based prediction model for moderate to severe acute postoperative pain after orthopedic surgery under general anesthesia.

Authors:
Shi G; Liu G; Gao Q; Zhang S; Wang Q and 3 more

Journal:
BMC Anesthesiol

Publication Year: 2023

DOI:
10.1186/s12871-023-02328-1

PMCID:
PMC10626723

PMID:
37932714

Journal Information

Full Title: BMC Anesthesiol

Abbreviation: BMC Anesthesiol

Country: Unknown

Publisher: Unknown

Language: N/A

Publication Details

Subject Category: Anesthesiology

Available in Europe PMC: Yes

Available in PMC: Yes

PDF Available: No

Transparency Score
3/6
50.0% Transparent
Transparency Indicators
Click on green indicators to view evidence text
Core Indicators
Data Sharing
Code Sharing
Evidence found in paper:

"Declarations Ethical approvalThe study was approved and monitored by the Ethics Committee of Shanxi Bethune Hospital (Third Hospital of Shanxi Medical University). Statement of human rightsAll procedures in this study were conducted in accordance with the Ethics Committee of Shanxi Bethune Hospital (Third Hospital of Shanxi Medical University) approved protocols. Statement of informed consentInformed consent for patient information to be published in this article was not obtained because of the retrospective nature of the study and the patient’s identity information has been concealed. The requirement for informed consent was waived by the Ethics Committee of Shanxi Bethune Hospital. Consent for publicationNot applicable. Competing interestsThe authors declare no competing interests. Competing interests The authors declare no competing interests."

Evidence found in paper:

"Funding This study was supported by the Department of Education of Shanxi Province [2021Y364], and the Key R&D Projects of Shanxi Province [202102130501003] & [201903D311011]."

Protocol Registration
Open Access
Paper is freely available to read
Additional Indicators
Replication
Novelty Statement
Assessment Info

Tool: rtransparent

OST Version: N/A

Last Updated: Aug 05, 2025