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.

"Weekend warrior" and regularly active physical activity patterns have been associated with reduced mortality risk in the general population. The association in patients with diabetes is unknown.

Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have been shown to decrease blood glucose levels, promote weight loss, and prevent cardiovascular events. However, evidence is limited regarding their effect on dementia, although emerging observational studies, some with serious methodological limitations, have suggested large reductions in dementia associated with GLP-1RAs that may not be entirely causally related.

Vaporized nicotine products (VNPs) are more effective than nicotine replacement therapy (NRT) for smoking cessation in general populations, but their effectiveness among low socioeconomic groups is largely unknown.

Glucagon-like peptide-1 receptor agonists (GLP-1 RAs), medications used to treat type 2 diabetes and obesity, are associated with delayed gastric emptying, which is a risk factor for gastroesophageal reflux disease (GERD). However, evidence linking these drugs to GERD is limited.

Aluminum is used as an adjuvant in nonlive vaccines administered in early childhood. Concerns persist about potential associations between vaccination with aluminum-adsorbed vaccines and increased risk for chronic autoimmunity, atopy or allergy, and neurodevelopmental disorders. Large-scale safety data remain limited.

Single-arm trials can be used to explore the feasibility, implementation, and effects of treatment. They typically use opportunistic convenience sampling to find potential participants. Their main limitations for health care decision making are lack of generalizability and the poor quality of the comparative evidence they produce. The authors propose a single-arm trial design that can provide greater generalizability and higher quality of comparative evidence than traditional single-arm trials, called a random invitation single-arm trial or RISAT. A RISAT has 4 essential components. First, it has a data infrastructure for routinely collected real-world data (RWD) where participants have provided consent to have their data used for research (for example, a registry or electronic medical record database). Second, a subset of those participants are randomly invited to take part in the RISAT. Those not invited are not contacted. Third, invitees are offered the specific intervention (such as a novel treatment), to which they consent or not. Fourth, all invitees are followed prospectively regardless of their acceptance of the intervention. For an optional randomized comparison, RISATs can use the RWD infrastructure to measure outcomes from invitees and noninvitees. The authors describe the advantages and challenges of this approach, including inferential issues and biases and comparison with other designs. They show how RISATs allow fairer access to participation, improve applicability and generalizability of results compared with traditional single-arm trials, and provide a form of randomized real-world evidence. At negligible added cost beyond already existing infrastructure, this approach may catalyze the early generation of evidence of higher value than that produced with traditional single-arm trials, increasing the credibility and validity of accelerated drug approval processes and enabling better health care decisions.

Violent injury survivors are at risk for revictimization. The St. Louis area hospital-based violence intervention program (HVIP), Life Outside of Violence (LOV), is the first multisystem, region-wide HVIP in the United States.

A 2020 consensus statement proposed body mass index (BMI)-specific waist circumference (WC) thresholds to improve patient care.

Drugs are approved when their benefits outweigh their risks based on the results of clinical trials. It remains unclear how often regulatory agencies extrapolate or restrict the approval compared with the trial.

The European Association for the Study of Obesity (EASO) recently introduced a new framework to define obesity that incorporates anthropometric measures beyond body mass index (BMI) and clinical comorbidities. However, this framework has not been validated.

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

Ablation of atrial fibrillation can restore normal heart rhythm, but its effect on clinical outcomes is uncertain.

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

Emergency Med: [Formula: see text] Critical Care: [Formula: see text] Pulmonology: [Formula: see text].

Risk adjustment is a critical component of health care reimbursement aimed at ensuring fair compensation on the basis of the characteristics of patients receiving care. Optimizing risk adjustment is not just a matter of improving efficiency or predictive accuracy; it is a crucial step toward achieving health equity by ensuring that resources are directed toward patients who need them most and reducing incentives to exclude or neglect high-risk patients. The authors reviewed available publications from PubMed and Google Scholar published between 2000 and 2025, as well as relevant news articles, policy documents, websites, and other sources related to risk adjustment and application areas. This process yielded 8 recommendations related to standardizing risk adjustment methods, promoting data interoperability, implementing strategies to enable more accurate and continuous reflections of patients' health status, integrating valid and reliable metrics into regular evaluation and feedback mechanisms, limiting "gaming" opportunities and incentives, creating valid ways to measure costs of caring for patients who are experiencing health care disparities and inequities and/or are disproportionately affected by social drivers of health, evaluating and leveraging advanced analytics and machine learning when able to improve risk adjustment models, and promoting research and implementation methods that combine elements of both prospective and concurrent risk adjustment. Implementation of these risk adjustment recommendations has broad implications for various entities in the health care ecosystem.

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

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

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

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