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Aims: In neuropsychological evaluations, it is often difficult to ascertain whether poor performance on measures of validity is due to poor effort or malingering, or whether there is genuine cognitive impairment. Dunham and Denney created an algorithm to assess this question using the Medical Symptom Validity Test (MSVT). We assessed the ability of their algorithm to detect poor validity versus probable impairment, and concordance of failure on the MSVT with other freestanding tests of performance validity.

Methods: Two previously published datasets (n?=?153 and n?=?641, respectively) from outpatient neuropsychological evaluations were used to test Dunham and Denney’s algorithm, and to assess concordance of failure rates with the Test of Memory Malingering and the forced choice measure of the California Verbal Learning Test, two commonly used performance validity tests.

Results: In both datasets, none of the four cutoff scores for failure on the MSVT (70%, 75%, 80%, or 85%) identified a poor validity group with proportionally aligned failure rates on other freestanding measures of performance validity. Additionally, the protocols with probable impairment did not differ from those with poor validity on cognitive measures.

Conclusions: Despite what appeared to be a promising approach to evaluating failure on the easy MSVT subtests when clinical data are unavailable (as recommended in the advanced interpretation program, or advanced interpretation [AI], of the MSVT), the current findings indicate the AI remains the gold standard for doing so. Future research should build on this effort to address shortcomings in measures of effort in neuropsychological evaluations.  相似文献   

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PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.  相似文献   
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BackgroundThe measurement of health-related quality of life (HRQoL) provides information about the perceived burden of the health condition and treatments from a lived experience. The Brisbane Burn Scar Impact Profile (caregiver report for young children, BBSIP0–8), developed in 2013, is a proxy-report measure of burn scar-specific HRQoL. The aim of this study was to report its psychometric properties in line with an evaluative purpose.MethodsCaregivers of children up to 8 years of age at risk of burn scarring were recruited into a prospective, longitudinal cohort study. Caregivers completed the BBSIP0–8, Pediatric Quality of Life Inventory and Patient Observer Scar Assessment Scale at baseline (approximately ≥85% of the total body surface area re-epithelialised), 1–2 weeks after baseline and 1-month after baseline. Psychometric properties measured included internal consistency, test–retest reliability, validity and responsiveness.ResultsEighty-six caregivers of mostly male children (55%), of a median age (IQR) of 1 year, 10 months (2 years, 1 month) and total body surface area burn of 1.5% (3.0%) were recruited. Over one third of participants were grafted and 15% had contractures or skin tightness at baseline. Internal consistency of ten item groups ranged from 0.73 to 0.96. Hypothesised correlations of changes in the BBSIP0–8 items with changes in criterion measures supported longitudinal validity (ρ ranging from ?0.73 to 0.68). The majority of item groups had acceptable reproducibility (ICC = 0.65–0.83). The responsiveness of five item groups was supported (AUC = 0.71–0.90).ConclusionThe psychometric properties tested support the use of the BBSIP0–8 as an evaluative measure of burn scar-related health-related quality of life for children aged below eight years in the early post-acute period of rehabilitation. Further investigation at longer time period after burn injury is indicated.  相似文献   
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薄层色谱鉴别在历版《中国药典》中的应用经历了从无到有、从少到多的过程;而薄层扫描含量测定在最近几版《中国药典》中的应用比例逐渐降低。随着对中药质量标准体系要求的进一步提高,薄层色谱法的不足之处陆续显现,如仪器普及率低、设备并不简单、结果重复性和稳定性较差、鉴别速度及准确性不及高效液相色谱法、展开剂毒性大等,逐渐不合时宜。在制定中药质量标准时,研究者不应该墨守成规,薄层色谱鉴别也不应该是雷打不动的定性鉴别必备选项。高效液相色谱法具备完全取代薄层色谱法的可行性,薄层色谱法可作为高效液相色谱法的补充。为充分降低检测成本、缩短检测周期、提高鉴别效率,笔者建议中药质量标准体系应该大幅减少薄层色谱鉴别方法的应用,增加高效液相特征图谱鉴定,尽量做到“一个条件,一张图谱”;除非确有必要,《中国药典》等国家质量标准体系应将薄层色谱鉴别作为推荐方法,而非强制标准。  相似文献   
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