Suppr超能文献

计算机化自适应测试在肘管综合征手术患者中患者评估量表(PEM)的应用。

Computerized adaptive testing for the patient evaluation measure (PEM) in patients undergoing cubital tunnel syndrome surgery.

机构信息

Department of Plastic, Reconstructive and Hand Surgery, Radboud University Medical Centre, Radboud Institute for Health Sciences, Nijmegen, Gelderland, The Netherlands.

Nufffield Department for Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK.

出版信息

J Hand Surg Eur Vol. 2023 Nov;48(10):1042-1047. doi: 10.1177/17531934231164959. Epub 2023 Apr 17.

Abstract

In outcome measures, item response theory (IRT) validation can deliver interval-scaled high-quality measurement that can be harnessed using computerized adaptive tests (CATs) to pose fewer questions to patients. We aimed to develop a CAT by developing an IRT model for the Patient Evaluation Measure (PEM) for patients undergoing cubital tunnel syndrome (CuTS) surgery. Nine hundred and seventy-nine completed PEM responses of patients with CuTS in the United Kingdom Hand Registry were used to develop and calibrate the CAT. Its performance was then evaluated in a simulated cohort of 1000 patients. The CAT reduced the original PEM length from ten to a median of two questions (range two to four), while preserving a high level of precision (median standard error of measurement of 0.27). The mean error between the CAT score and full-length score was 0.08%. A Bland-Altman analysis showed good agreement with no signs of bias. The CAT version of the PEM can substantially reduce patient burden while enhancing construct validity by harnessing IRT for patients undergoing CuTS surgery.

摘要

在结果衡量标准中,项目反应理论(IRT)验证可以提供区间标度的高质量测量,这些测量可以通过计算机自适应测试(CAT)来利用,从而减少对患者的问题数量。我们旨在通过为接受肘管综合征(CuTS)手术的患者开发一个 IRT 模型,来开发一个 CAT。使用来自英国手登记处的 979 名完成了肘部管综合征患者评估量表(PEM)的患者的回复,来开发和校准 CAT。然后,在模拟的 1000 名患者队列中评估其性能。CAT 将原始 PEM 的长度从十个问题减少到中位数为两个问题(范围为两个到四个),同时保持高精度(中位数测量标准误差为 0.27)。CAT 分数与全长分数之间的平均误差为 0.08%。Bland-Altman 分析显示,没有偏差的迹象,一致性很好。PEM 的 CAT 版本可以通过为接受 CuTS 手术的患者利用 IRT 来大大减轻患者负担,同时增强结构有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e267/10616996/461ec3cfc0af/10.1177_17531934231164959-fig1.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