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| http://dx.doi.org/10.3399/bjgp25X740493 | DOI Listing |
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Validation, bias assessment, and optimization of the UNAFIED 2-year risk prediction model for undiagnosed atrial fibrillation using national electronic health data.
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Pfizer Inc, New York, New York.
Background: Prediction models for atrial fibrillation (AF) may enable earlier detection and guideline-directed treatment decisions. However, model bias may lead to inaccurate predictions and unintended consequences.
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Disparities in cervical cancer elimination timeframes in the United States: a comparative modeling study.
J Natl Cancer Inst
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Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands.
Population-level estimates in timeframes for reaching cervical cancer (CC) elimination (ie, <4 cases per 100,000 women) in the United States may mask potential disparities in achieving elimination among sub-populations. We used three independent Cancer Intervention and Surveillance Modeling Network (CISNET) models to estimate differences in the time to CC elimination across seven strata of correlated screening and human papillomavirus vaccination uptake, based on national survey data. Compared to the average population, elimination was achieved ≥22 years earlier for the high-uptake strata and ≥27 years later for the most extreme low-uptake strata.
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Epilepsy is a prevalent neurological disorder characterized by seizures that significantly impact individuals and their social environments. Given the unpredictable nature of epileptic seizures, developing automated epilepsy diagnosis systems is increasingly important. Epilepsy diagnosis traditionally relies on analyzing EEG signals, with recent deep learning methods gaining prominence due to their ability to bypass manual feature extraction.
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Joint Reconstruction Research Center (JRRC), Imam Khomeini Hospital, Tehran University of Medical Sciences, End of Keshavarz Blvd, 1419733141 Tehran, Iran. Electronic address:
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View Article and Find Full Text PDFValidating quality standards in Palestinian emergency departments: An e-Delphi survey approach.
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Division of Emergency Medicine, University of Cape Town, Cape Town, South Africa.
To validate Palestine's previously derived emergency department quality standards (EDQS) using an e-Delphi survey. A two-round e-Delphi survey validated the EDQS, developed in an earlier study through a literature review and consensus-building among Palestinian emergency medicine and healthcare quality experts. The study purposively sampled 53 emergency department and healthcare quality experts with over 5 years of experience.
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