Cross-validation of the Dot Counting Test in a large sample of credible and non-credible patients referred for neuropsychological testing.

CLINICAL NEUROPSYCHOLOGIST(2018)

引用 28|浏览11
暂无评分
摘要
Objective: To cross-validate the Dot Counting Test in a large neuropsychological sample. Method: Dot Counting Test scores were compared in credible (n = 142) and non-credible (n = 335) neuropsychology referrals. Results: Non-credible patients scored significantly higher than credible patients on all Dot Counting Test scores. While the original E-score cut-off of >= 17 achieved excellent specificity (96.5%), it was associated with mediocre sensitivity (52.8%). However, the cut-off could be substantially lowered to >= 13.80, while still maintaining adequate specificity (>= 90%), and raising sensitivity to 70.0%. Examination of non-credible subgroups revealed that Dot Counting Test sensitivity in feigned mild traumatic brain injury (mTBI) was 55.8%, whereas sensitivity was 90.6% in patients with non-credible cognitive dysfunction in the context of claimed psychosis, and 81.0% in patients with non-credible cognitive performance in depression or severe TBI. Thus, the Dot Counting Test may have a particular role in detection of non-credible cognitive symptoms in claimed psychiatric disorders. Alternative to use of the E-score, failure on >= 1 cut-offs applied to individual Dot Counting Test scores (>= 6.0" for mean grouped dot counting time, >= 10.0" for mean ungrouped dot counting time, and >= 4 errors), occurred in 11.3% of the credible sample, while nearly two-thirds (63.6%) of the non-credible sample failed one of more of these cut-offs. Conclusions: An E-score cut-off of 13.80, or failure on >= 1 individual score cut-offs, resulted in few false positive identifications in credible patients, and achieved high sensitivity (64.0-70.0%), and therefore appear appropriate for use in identifying neurocognitive performance invalidity.
更多
查看译文
关键词
Dot Counting Test,Performance Validity,non-credible performance,freestanding performance validity test
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要