Suppr超能文献

剑桥膝关节损伤工具(CamKIT):一种用于急性膝关节软组织损伤的临床预测工具。

The Cambridge Knee Injury Tool (CamKIT): a clinical prediction tool for acute soft tissue knee injuries.

作者信息

Molloy Thomas, Gompels Benjamin, Castagno Simone, McDonnell Stephen

机构信息

Division of Trauma and Orthopaedic Surgery, University of Cambridge, Cambridge, UK.

Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia.

出版信息

BMJ Open Sport Exerc Med. 2025 Jan 27;11(1):e002357. doi: 10.1136/bmjsem-2024-002357. eCollection 2025.

Abstract

BACKGROUND/AIM: This study focuses on the development of the Cambridge Knee Injury Tool (CamKIT), a clinical prediction tool developed as a 12-point scoring tool based on a modified e-Delphi study.

METHODS

A retrospective cohort evaluation was conducted involving 229 patients presenting to a Major Trauma Centre with acute knee pain over 3 months. The evaluation extracted data on the 12 scoring tool variables as well as diagnostic and management pathway outcomes. CamKIT scores for the injured and non-injured cohorts were then calculated and evaluated.

RESULTS

The CamKIT yielded a median score of 7.5 (IQR: 6-9) in the injured cohort, compared with a median score of 2 (IQR: 1-4) in the non-injured cohort, with a statistically significant difference (p<0.0001). When constructed as a three-tier risk stratification tool, the CamKIT produces a sensitivity of 100%, a specificity of 94.3%, a positive predictive value of 89% and a negative predictive value of 100% for diagnosing clinically significant soft tissue knee injuries.

CONCLUSION

The CamKIT provides a non-invasive tool that has the potential to streamline the diagnostic process and empower healthcare workers in resource-stretched settings by instilling confidence and promoting accuracy in clinical decision-making. The CamKIT also has the potential to support efficiency in the secondary healthcare setting by enabling more targeted and timely use of specialist resources. This research contributes to the ongoing efforts to enhance patient outcomes and the overall quality of care in managing acute knee injuries.

摘要

背景/目的:本研究聚焦于剑桥膝关节损伤工具(CamKIT)的开发,这是一种基于改良的电子德尔菲研究开发的临床预测工具,为12分制评分工具。

方法

对一家主要创伤中心3个月内出现急性膝关节疼痛的229例患者进行回顾性队列评估。该评估提取了12项评分工具变量的数据以及诊断和管理途径结果。然后计算并评估受伤组和未受伤组的CamKIT评分。

结果

受伤组的CamKIT中位数评分为7.5(四分位间距:6 - 9),而未受伤组的中位数评分为2(四分位间距:1 - 4),差异有统计学意义(p<0.0001)。当构建为三层风险分层工具时,CamKIT对诊断具有临床意义的膝关节软组织损伤的敏感性为100%,特异性为94.3%,阳性预测值为89%,阴性预测值为100%。

结论

CamKIT提供了一种非侵入性工具,有可能简化诊断过程,并通过增强信心和提高临床决策的准确性,使资源紧张环境中的医护人员更有能力。CamKIT还有可能通过更有针对性和及时地使用专科资源,支持二级医疗保健环境中的效率提升。这项研究有助于持续努力改善急性膝关节损伤患者的治疗效果和整体护理质量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f73f/11780958/fc7681e4c276/bmjsem-11-1-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验