Background: The HIV Dementia Scale (HDS) was developed to screen for HIV-associated neurocognitive disorders (HAND), but concerns have persisted regarding its substandard sensitivity. This study aimed to examine the classification accuracy of the HDS using raw and norm-based cut points and to evaluate the contribution of the HDS subtests to predicting HAND.
Methods: A total of 1580 HIV-infected participants from 6 US sites completed the HDS, and a gold standard neuropsychological battery, on which 51% of participants were impaired.
Results: Sensitivity and specificity to HAND using the standard raw HDS cut point were 24% and 92%, respectively. The raw HDS subtests of attention, recall, and psychomotor speed significantly contributed to classification of HAND, whereas visuomotor construction contributed the least. A modified raw cut point of 14 yielded sensitivity of 66% and specificity of 61%, with cross-validation. Using norms also significantly improved sensitivity to 69% with a concomitant reduction of specificity to 56%, whereas the positive predictive value declined from 75% to 62% and negative predictive value improved from 54% to 64%. The HDS showed similarly modest rates of sensitivity and specificity among subpopulations of individuals with minimal comorbidity and successful viral suppression.
Conclusions: Findings indicate that while the HDS is a statistically significant predictor of HAND, particularly when adjusted for demographic factors, its relatively low diagnostic classification accuracy continues to hinder its clinical utility. A raw cut point of 14 greatly improved the sensitivity of the previously established raw cut score, but may be subject to ceiling effects, particularly on repeat assessments.
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http://dx.doi.org/10.1097/QAI.0b013e318278ffa4 | DOI Listing |
Front Plant Sci
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College of Engineering, South China Agricultural University, Guangzhou, China.
Introduction: Accurate detection and recognition of tea bud images can drive advances in intelligent harvesting machinery for tea gardens and technology for tea bud pests and diseases. In order to realize the recognition and grading of tea buds in a complex multi-density tea garden environment.
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Front Plant Sci
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Information and Communication Engineering, Yeungnam University, Gyeongsan, Republic of Korea.
Smart farming is a hot research area for experts globally to fulfill the soaring demand for food. Automated approaches, based on convolutional neural networks (CNN), for crop disease identification, weed classification, and monitoring have substantially helped increase crop yields. Plant diseases and pests are posing a significant danger to the health of plants, thus causing a reduction in crop production.
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School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
Significant strides have been made in emotion recognition from Electroencephalography (EEG) signals. However, effectively modeling the diverse spatial, spectral, and temporal features of multi-channel brain signals remains a challenge. This paper proposes a novel framework, the Directional Spatial and Spectral Attention Network (DSSA Net), which enhances emotion recognition accuracy by capturing critical spatial-spectral-temporal features from EEG signals.
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Biomedical Engineering Department, College of Engineering, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.
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Department of Computer and Automatic Control, Faculty of Engineering, Tanta University, Tanta, Egypt.
Introduction: Diabetes prediction using clinical datasets is crucial for medical data analysis. However, class imbalances, where non-diabetic cases dominate, can significantly affect machine learning model performance, leading to biased predictions and reduced generalization.
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