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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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http://dx.doi.org/10.1002/lary.31467 | DOI Listing |
JCO Glob Oncol
May 2025
Department of Biochemistry, All India Institute of Medical Sciences, New Delhi, India.
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JCO Glob Oncol
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Grupo Oncoclínicas, São Paulo, Brazil.
Head and neck squamous cell carcinoma (HNSCC) represents a significant public health burden in developing countries, where access to early diagnosis, comprehensive care, and research infrastructure is limited. This article synthesizes the insights generated during a Fireside Chat convened by members of the Latin American Cooperative Oncology Group (LACOG)-Head and Neck and the Brazilian Group of Head and Neck Cancer (GBCP), with the participation of international expert Professor Hisham Mehanna. The discussion addressed key challenges and opportunities in clinical and translational research within resource-constrained settings.
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November 2025
Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
Background And Objectives: Myelitis is a relatively common clinical entity for neurologists, with diverse underlying causes. The aim of this study was to describe the incidence of myelitis, its causes, clinical presentation, and factors predicting functional outcomes and relapses.
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Neurol Neuroimmunol Neuroinflamm
November 2025
Departments of Neurology and Ophthalmology, NYU Grossman School of Medicine, NY; and.
Background And Objectives: While reductions in optical coherence tomography (OCT) pRNFL and ganglion cell-inner plexiform layer thicknesses have been shown to be associated with brain atrophy in adult-onset MS (AOMS) cohorts, the relationship between OCT and brain MRI measures is less established in pediatric-onset MS (POMS). Our aim was to examine the associations of OCT measures with volumetric MRI in a cohort of patients with POMS to determine whether OCT measures reflect CNS neurodegeneration in this patient population, as is seen in AOMS cohorts.
Methods: This was a cross-sectional study with retrospective ascertainment of patients with POMS evaluated at a single center with expertise in POMS and neuro-ophthalmology.
Emerg Top Life Sci
September 2025
Hurdle.bio / Chronomics Ltd., London, UK.
Artificial intelligence (AI) is transforming many fields, including healthcare and medicine. In biomarker discovery, AI algorithms have had a profound impact, thanks to their ability to derive insights from complex high-dimensional datasets and integrate multi-modal datatypes (such as omics, electronic health records, imaging or sensor and wearable data). However, despite the proliferation of AI-powered biomarkers, significant hurdles still remain in translating them to the clinic and driving adoption, including lack of population diversity, difficulties accessing harmonised data, costly and time-consuming clinical studies, evolving AI regulatory frameworks and absence of scalable diagnostic infrastructure.
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