How soft are neurological soft signs? Content overlap analysis of 71 symptoms among seven most commonly used neurological soft signs scales. (June 2021)

• Record Type: Journal Article
• Authors: Chrobak, Adrian Andrzej; Krupa, Anna; Dudek, Dominika; Siwek, Marcin
• Is Part Of: Journal of psychiatric research. Volume 138(2021)
• Page Start: 404
• Page End: 412
• Publication Date: 2021-06
• Subjects: Neurological soft signs -- Content analysis -- Rating scales -- Symptom overlap -- Schizophrenia; Psychiatry -- Periodicals; Mental Disorders -- Periodicals; Maladies mentales -- Périodiques; Psychiatry; Electronic journals; Periodicals; 616.89005
• DOI: 10.1016/j.jpsychires.2021.04.020
• Languages: English
• ISSNs: 0022-3956

Abstract

Abstract: Introduction: Neurological soft signs (NSS) are described as subtle, non-localizable neurological abnormalities that cannot be related to impairment of a specific brain region or are not believed to be typical for any specific neurological disease. Crucial issue concerning research on NSS are the instruments with which they are assessed, since the results and the conclusions of the studies are mediated by the characteristics of such instruments. There is common, silent and unverified assumption that NSS rating scales may be used as interchangeable measure of the same phenomenon. Aim: To investigate the differences in item content and the interchangeability of commonly used NSS scales. Methods: A content analysis was carried out to determine symptom overlap among the chosen seven most often used scales using the Jaccard index (0 = no overlap, 1 = full overlap) according to the methodology of Fried 2017. Results: 71 NSSs were distinguished from 167 items used in 7 above mentioned instruments. Mean overlap among all scales is low (0.27), overlap among specific scales ranges from 0.1 to 0.5. Conclusions: The diversity of NSS in analyzed tools causes the low overlap between scales, leading to uncertainty as to whether they measure the same phenomena. This limits the reproducibility of studies and impedes the possibility of unifying the knowledge stemming from existing data. We argue that the non-localizable nature of NSS is yet to be examined.