Scientificity, transparency and applicability of Chinese guidelines and consensuses in medical imaging published in 2022
10.3760/cma.j.cn112149-20231104-00348
- VernacularTitle:2022年医学期刊发表中国影像医学领域指南及共识的科学性、透明性和适用性的评价分析
- Author:
Han LYU
1
;
Qi ZHOU
;
Jun LIU
;
Han WANG
;
Zhenchang WANG
;
Yaolong CHEN
Author Information
1. 首都医科大学附属北京友谊医院放射科，北京 100050
- Keywords:
Guidebooks;
Consensus;
Medical imaging;
Quality control and improvement
- From:
Chinese Journal of Radiology
2024;58(4):430-436
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To evaluate the scientificity, transparency and applicability of the Chinese guidelines and consensuses in medical imaging published in 2022 by the STAR scale.Methods:Medical imaging guidelines and consensuses were searched in CNKI, Wanfang data, CMB, Chinese Medical Journal Network, and Medline (PubMed). The publication date was selected from January 1 to December 31, 2022. Each guideline or consensus was independently evaluated and cross-checked by two evaluators using STAR scale.Results:A total of 65 guidelines and consensus that were published as Chinese or English were included, including 15 guidelines and 50 consensuses. Some guidelines and consensus have distinct disciplinary characteristics with topics such as artificial intelligence (4 articles) and Evidence-Based Medical Imaging-Medical Imaging Clinical Appropriateness (EB-MICA, 4 articles). In all guidelines and consensuses, the highest score was 89.9, the lowest was 3.6, and the M( Q1, Q3) was 25.0 (20.8, 35.4). There was no statistical difference in the scores of guidelines and consensuses ( P=0.383). The highest scoring areas were recommendation opinions (reporting rate of 56.0%), working groups (reporting rate of 38.2%), and clinical issues (reporting rate of 36.7%), while the lowest scoring areas were proposal (reporting rate of 9.6%), registration (reporting rate of 10.8%), and consensus methods (reporting rate of 21.8%). Conclusion:It is recommended that guidelines and consensuses initiators of medical imaging strengthen the learning of evidence-based medicine methods, such as STAR tools, in order to further improve the quality of guidelines and consensuses of medical imaging.
