GIM/FP/GP: [Formula: see text] Cardiology: [Formula: see text].

GIM/FP/GP: [Formula: see text] Pulmonology: [Formula: see text].

GIM/FP/GP: [Formula: see text] Pulmonology: [Formula: see text].

GIM/FP/GP: [Formula: see text] Oncology: [Formula: see text] Hematology: [Formula: see text].

GIM/FP/GP: [Formula: see text] Endocrinology: [Formula: see text].

GIM/FP/GP: [Formula: see text] Cardiology: [Formula: see text].

Engineering infection controls include a wide range of interventions used indoors to reduce occupants' exposure to respiratory pathogens.

Medial opening wedge high tibial osteotomy (HTO) is a limb realignment surgery that aims to preserve joint structure and improve clinical outcomes by redistributing ambulatory loads on the knee among patients with knee osteoarthritis and varus alignment.

Eosinophilic granulomatosis with polyangiitis (EGPA) is an eosinophilic antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis. Rituximab has emerged as the standard of care in other types of ANCA-associated vasculitis, but controlled studies on its use in EGPA are yet lacking.

Social determinants of health (SDoH) contribute to disparities in SARS-CoV-2 infection, but their associations with long COVID are unknown.

Clinicians increasingly rely on prediction models to guide treatment choices. Most prediction models, however, are developed using observational data that include some patients who have already received the treatment the prediction model is meant to inform. Special attention to the causal role of those earlier treatments is required when interpreting the resulting predictions. "Causal blind spots" were identified in 3 common approaches to handling treatment when developing a prediction model: including treatment as a predictor, restricting to persons taking a certain treatment, and ignoring treatment. Through several real examples, this article illustrates how the risks obtained from models developed using such approaches may be misinterpreted and can lead to misinformed decision making. The discussion covers issues attributable to confounding, selection, mediation, and changes in treatment protocols over time. An extension of guidelines for the development, reporting, and evaluation of prediction models is advocated to avoid such misinterpretations. Developers must ensure that the intended target population for the model, and the treatment conditions under which predictions hold, are clearly communicated. When prediction models are intended to inform treatment decisions, they need to provide estimates of risk under the specific treatment (or intervention) options being considered, known as "prediction under interventions." Next to suitable data, this requires causal reasoning and causal inference techniques during model development and evaluation. Being clear about what a given prediction model can and cannot be used for prevents misinformed treatment decisions and thereby prevents potential harm to patients.