Network Meta-Analysis: Comparison of Endoscopic Dysplasia Detection Technologies in Inflammatory Bowel Disease.

Background: Novel colorectal cancer endoscopic surveillance techniques for inflammatory bowel disease (IBD) have recently been developed.

Aims: Compare the efficacy of currently available techniques for dysplasia detection in colonic IBD.

Methods: We conducted a systematic literature search from inception to March 2024 for randomized controlled trials (RCTs) or prospective cohort studies enrolling adults with IBD and having surveillance colonoscopy for dysplasia screening.

Eligibility Criteria of Randomized Clinical Trials in Critical Care Medicine.

Importance: Eligibility criteria for randomized clinical trials (RCTs) are designed to select clinically relevant patient populations. However, not all eligibility criteria are strongly justified, potentially excluding marginalized groups, and limiting the generalizability of trial findings.

Objective: To summarize and evaluate the justification of exclusion criteria in published RCTs in critical care medicine.

Improving the Robustness of Deep-Learning Models in Predicting Hematoma Expansion from Admission Head CT.

Background And Purpose: Robustness against input data perturbations is essential for deploying deep-learning models in clinical practice. Adversarial attacks involve subtle, voxel-level manipulations of scans to increase deep-learning models' prediction errors. Testing deep-learning model performance on examples of adversarial images provides a measure of robustness, and including adversarial images in the training set can improve the model's robustness.

An Elusive Diagnosis of Castleman Disease.

We present the case of a 41-year-old man with an anterior mediastinal mass and constellation of clinical symptoms, including dyspnea, pleural effusions, pericardial effusions, renal insufficiency, and pancytopenia. After inconclusive results on several laboratory tests and a nondiagnostic surgical biopsy specimen, a specimen from a second surgical biopsy identified the patient's condition as Castleman disease associated with TAFRO (thrombocytopenia, anasarca, fevers, reticulin myelofibrosis, organomegaly) syndrome. This case highlights the importance of obtaining large tissue biopsy samples, interval follow-up, and acknowledging cognitive biases.

Machine Learning Models for 3-Month Outcome Prediction Using Radiomics of Intracerebral Hemorrhage and Perihematomal Edema from Admission Head Computed Tomography (CT).

: Intracerebral hemorrhages (ICH) and perihematomal edema (PHE) are respective imaging markers of primary and secondary brain injury in hemorrhagic stroke. In this study, we explored the potential added value of PHE radiomic features for prognostication in ICH patients. : Using a multicentric trial cohort of acute supratentorial ICH ( = 852) patients, we extracted radiomic features from ICH and PHE lesions on admission non-contrast head CTs.