Publications by authors named "D A Wood"

Background: Breast implant surfaces are categorized as smooth or textured. Compared with smooth implants, textured surface implants have a higher risk of breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) but may have a lower risk of capsular contracture (CC). This study aimed to quantify whether survey respondents would be willing to accept a higher risk of BIA-ALCL in exchange for the potential reported benefits of textured breast implants.

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Background: Accurate and comprehensive identification of enteropathogens, causing infectious gastroenteritis, is essential for optimal patient treatment and effective isolation processes in health care systems. Traditional diagnostic techniques are well established and optimised in low-cost formats. However, thorough testing for a wider range of causal agents is time consuming and remains limited to a subset of pathogenic organisms.

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The NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Lung Cancer Screening provide criteria for selecting individuals for screening and offer recommendations for evaluating and managing lung nodules detected during initial and subsequent annual screening. These NCCN Guidelines Insights focus on recent updates to the NCCN Guidelines for Lung Cancer Screening.

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Background: Accurate estimates of personal exposure to ambient air pollution are difficult to obtain and epidemiological studies generally rely on residence-based estimates, averaged spatially and temporally, derived from monitoring networks or models. Few epidemiological studies have compared the associated health effects of personal exposure and residence-based estimates.

Objective: To evaluate the association between exposure to air pollution and cognitive function using exposure estimates taking mobility and location into account.

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Artificial intelligence (AI) tools can triage radiology scans to streamline the patient pathway and also relieve clinician workload. Validated AI tools can mitigate the delays in reporting scans by flagging time-sensitive and actionable findings. In this study, we aim to investigate current stakeholder perspectives and identify obstacles to integrating AI in clinical pathways.

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