Objectives: Research samples that are representative of patient populations are needed to ensure the generalizability of study findings. The primary aim was to assess the efficacy of a study design and recruitment strategy in obtaining a participant sample that was representative of the broader cochlear implant (CI) patient population at the CI center. A secondary aim was to review whether the CI recipient population was representative of the state population.
Methods: Demographic variables were compared for a research participant sample (n = 79) and the CI patient population (n = 338). The participant sample was recruited from the CI patient population. The study design included visits that were at the same location and frequency as the recommended clinical follow-up intervals. The demographics for the combined group (participant sample and patient population) were then compared to the reported demographics for the population in North Carolina.
Results: There were no significant differences between the participant sample and patient population for biological sex, age at implantation, racial distribution, socioeconomic position, degree of urbanization, or drive time to the CI center (p ≥ 0.086). The combined CI recipient population was significantly different from the North Carolina population for the distributions of race, ethnicity, and degree of urbanization (p < 0.001).
Conclusion: The study design and recruitment strategy allowed for recruitment of a participant sample that was representative of the CI patient population. Disparities in access to cochlear implantation persist, as supported by the significant differences in the combined CI recipient population and the population for our state.
Level Of Evidence: 3 Laryngoscope, 134:4101-4110, 2024.
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December 2024
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Department of Public Health, Ministry of Health, P.O. Box 24923, Kuwait City 13110, Kuwait.
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November 2024
Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.
In this study, we introduce a novel approach that integrates interpretability techniques from both traditional machine learning (ML) and deep neural networks (DNN) to quantify feature importance using global and local interpretation methods. Our method bridges the gap between interpretable ML models and powerful deep learning (DL) architectures, providing comprehensive insights into the key drivers behind model predictions, especially in detecting outliers within medical data. We applied this method to analyze COVID-19 pandemic data from 2020, yielding intriguing insights.
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