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Transl Vis Sci Technol
March 2025
Department of Ophthalmology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Purpose: To examine the association of race, ethnicity, and other social determinants of health (SDH) on receipt of optic nerve (ON) evaluation in accordance with National Quality Forum (NQF) and the American Academy of Ophthalmology (AAO) guideline-based metrics.
Methods: This was a retrospective cohort study consisting of 13,582 patients with POAG receiving care across 12 tertiary care health. The odds of receiving ≥1 ON evaluations to monitor for glaucoma progression over 45 months of follow-up was evaluated.
Kardiol Pol
March 2025
Department of Electrocardiology, St. John Paul II Hospital, Kraków, Poland.
J Am Geriatr Soc
March 2025
Durham Veterans Affairs Geriatric Research Education and Clinical Center, Durham Veterans Affairs Health Care System (VAHCS), Durham, North Carolina, USA.
Background: Psychological resilience has been characterized as the ability to recover from stressful life events. Not well studied is whether self-reported measures of psychological resilience are associated with physical function recovery. Therefore, we examined the association of self-reported psychological resilience with longitudinal physical function before and after an acute care encounter.
View Article and Find Full Text PDFInt J Cancer
March 2025
International Agency for Research on Cancer, Lyon, France.
The Nordic countries benefited from declines in cervical cancer incidence rates due to the implementation of screening programmes. However, it is unclear whether all social groups have equally benefited from these preventive services. We provide an assessment of the temporal trends in cervical cancer incidence by socioeconomic position (SEP) in Denmark, Norway, Finland and Sweden, using data from the Nordic Occupational Cancer Study.
View Article and Find Full Text PDFStat Med
March 2025
Faculté de Pharmacie, Université de Montréal, Québec, Canada.
Targeted maximum likelihood estimation (TMLE) is an increasingly popular framework for the estimation of causal effects. It requires modeling both the exposure and outcome but is doubly robust in the sense that it is valid if at least one of these models is correctly specified. In addition, TMLE allows for flexible modeling of both the exposure and outcome with machine learning methods.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!