Outcome of patients with solid malignancies considered for intensive care unit admission: a single-center prospective cohort study.

Purpose: To identify the predictors and outcomes of ICU triage decisions in patients with solid malignancies (SM) and to investigate the usefulness of the National Early Warning Score (NEWS) and quick Sequential Organ Failure Assessment (qSOFA) score at triage.

Methods: All patients with SM for whom ICU admission was requested between July 2019 and December 2021 in a French university-affiliated hospital were included prospectively.

Results: Of the 6262 patients considered for ICU admission, 410 (6.

A multi-institutional meningioma MRI dataset for automated multi-sequence image segmentation.

Meningiomas are the most common primary intracranial tumors and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on brain MRI for diagnosis, treatment planning, and longitudinal treatment monitoring. However, automated, objective, and quantitative tools for non-invasive assessment of meningiomas on multi-sequence MR images are not available.

Differences in educational opportunity predict white matter development.

Coarse measures of socioeconomic status, such as parental income or parental education, have been linked to differences in white matter development. However, these measures do not provide insight into specific aspects of an individual's environment and how they relate to brain development. On the other hand, educational intervention studies have shown that changes in an individual's educational context can drive measurable changes in their white matter.

FEMA: Fast and efficient mixed-effects algorithm for large sample whole-brain imaging data.

The linear mixed-effects model (LME) is a versatile approach to account for dependence among observations. Many large-scale neuroimaging datasets with complex designs have increased the need for LME; however LME has seldom been used in whole-brain imaging analyses due to its heavy computational requirements. In this paper, we introduce a fast and efficient mixed-effects algorithm (FEMA) that makes whole-brain vertex-wise, voxel-wise, and connectome-wide LME analyses in large samples possible.