Publications by authors named "B E Flannery"
The test-negative design (TND) is widely used to estimate COVID-19 vaccine effectiveness (VE). Biased estimates of VE may result from effects of at-home SARS-CoV-2 rapid diagnostic test (RDT) results on decisions to seek healthcare. To investigate magnitude of potential bias, we constructed decision trees with input probabilities obtained from longitudinal surveys of U.
View Article and Find Full Text PDFBackground: The 2023-24 U.S. influenza season was characterized by a predominance of A(H1N1)pdm09 virus circulation with co-circulation of A(H3N2) and B/Victoria viruses.
View Article and Find Full Text PDFBackground: The 2023-2024 influenza season had predominant influenza A(H1N1)pdm09 virus activity, but A(H3N2) and B viruses co-circulated. Seasonal influenza vaccine strains were well-matched to these viruses.
Methods: Using health care encounters data from health systems in 8 states, we evaluated influenza vaccine effectiveness (VE) against influenza-associated medical encounters from October 2023-April 2024.
Article Synopsis
- Machine learning, particularly deep learning with convolutional neural networks (CNNs), is being used to detect prostate cancer in tissue slides, but sample type differences affect model accuracy.
- Research tested whether CNNs trained on one type of sample (biopsy or radical prostatectomy) could effectively analyze the other type, revealing a significant drop in performance across sample types.
- Results indicated that models performed well on their own sample but poorly on the alternative type, highlighting the need to consider morphological differences in training to improve cancer detection accuracy in clinical settings.*
Background: The 2023-24 U.S. influenza season was characterized by a predominance of A(H1N1)pdm09 virus circulation with co-circulation of A(H3N2) and B/Victoria viruses.
View Article and Find Full Text PDF