Parkinson's Disease Gravity Index: A Method by means of Optimal Scaling.

IF 1.7 Q4 NEUROSCIENCES
Neurology Research International Pub Date : 2020-12-15 eCollection Date: 2020-01-01 DOI:10.1155/2020/8871870
Marcos Serrano-Dueñas, Luis Masabanda, Maria-Rosario Luquin
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引用次数: 0

Abstract

Objective: This study has been designed with the aim of using optimal scaling to perform the allocation of scores and to be able to construct an indicator of the Parkinson's Disease Gravity Index. Scores were assigned to interrelated dimensions that share information about the patient's situation, to have an objective, holistic tool which integrates scores so that doctors can have a comprehensive idea of the patient's situation. Patients and Methods. 120 consecutive patients with Parkinson's diagnosis were chosen according to the United Kingdom Parkinson's Disease Society Brain Bank criteria. Subsequently, all the chosen dimensions were transformed into interval variables for which the formula proposed by Sturges was used. Once the dimensions were transformed into interval variables, optimal scaling was carried out. Subsequently, the following attributes were analyzed: quality and acceptability of the data; reliability: internal consistency, reliability index, Cronbach's alpha, and standard error of measurement; finally, validity: convergent validity and validity for known groups.

Results: There were no missing data. An appropriate Cronbach's alpha value of 0.71 was gathered, and all items were found to be pertinent to the scale. The item homogeneity index was 0.36. Precision evaluated with the standard error of measurement was 7.8. The Parkinson's Disease Gravity Index discriminant validity (validity for known groups), assessed among the different stages of Hoehn and Yahr scale by the Kruskal-Wallis test, showed major significance (X 2 = 32.7, p ≤ 0.001).

Conclusions: The Parkinson's Disease Gravity Index has shown adequate metric properties.

Abstract Image

帕金森病重力指数:通过优化比例的方法。
研究目的本研究旨在使用最佳比例来分配分数,并构建帕金森病重力指数指标。将分数分配给相互关联的维度,这些维度共享有关患者情况的信息,从而形成一个客观、全面的工具,将分数整合在一起,使医生能够全面了解患者的情况。患者和方法根据英国帕金森病协会脑库标准,连续挑选了 120 名帕金森病患者。随后,将所有选定的维度转化为区间变量,并使用 Sturges 提出的公式进行计算。将维度转换为区间变量后,进行了优化缩放。随后,对以下属性进行了分析:数据的质量和可接受性;可靠性:内部一致性、可靠性指数、克朗巴赫α和测量标准误差;最后是有效性:收敛有效性和已知群体的有效性:结果:没有数据缺失。Cronbach's alpha 值为 0.71,所有项目均与量表相关。项目同质性指数为 0.36。用测量标准误差评估的精确度为 7.8。通过 Kruskal-Wallis 检验对 Hoehn 和 Yahr 量表不同阶段的帕金森病重力指数判别效度(已知组别效度)进行了评估,结果显示其具有显著性(X 2 = 32.7,P ≤ 0.001):帕金森病重力指数显示了适当的度量特性。
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来源期刊
CiteScore
3.50
自引率
0.00%
发文量
10
审稿时长
17 weeks
期刊介绍: Neurology Research International is a peer-reviewed, Open Access journal that publishes original research articles, review articles, and clinical studies focusing on diseases of the nervous system, as well as normal neurological functioning. The journal will consider basic, translational, and clinical research, including animal models and clinical trials.
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