Perioperative Mobility Assessment of Acromegalic Patients Undergoing Endoscopic Endonasal Resection of Pituitary Adenomas Using Digital Phenotyping.

Methods to assess quality of life and recovery after endoscopic endonasal surgery (EES) for sellar lesions are limited and often biased by subjective patient-reported assessments. Objective in-situ assessments are lacking. Smartphone-based digital phenotyping has been increasingly studied across a variety of pathologies, utilizing built-in technologies to measure behavioral patterns pertaining to sleep, physical mobility, social interactions, and cognitive functioning, among others.

Traditional Machine Learning Methods versus Deep Learning for Meningioma Classification, Grading, Outcome Prediction, and Segmentation: A Systematic Review and Meta-Analysis.

Background: Meningiomas are common intracranial tumors. Machine learning (ML) algorithms are emerging to improve accuracy in 4 primary domains: classification, grading, outcome prediction, and segmentation. Such algorithms include both traditional approaches that rely on hand-crafted features and deep learning (DL) techniques that utilize automatic feature extraction.

Chronic endometritis: screening, treatment, and pregnancy outcomes in an academic fertility center.

Purpose: To identify the prevalence of chronic endometritis (CE), compare the efficacy of antibiotic regimens for CE, and examine pregnancy outcomes after treatment for CE among patients in an academic fertility clinic.

Methods: In this retrospective cohort study, data from patients who underwent endometrial sampling (ES) for CE evaluation at a single academic institution from 2014 to 2020 were collected and analyzed. Rates of CE were compared by indication for ES including recurrent pregnancy loss (RPL), implantation failure (IF), and recent first-trimester pregnancy loss.

A novel 6-day cycle surgical pathology rotation improves resident satisfaction and maintains Accreditation Council for Graduate Medical Education (ACGME) milestone performance.

Surgical pathology residency training in the United States lags behind other specialties in quality control and graduated responsibility to train independent pathologists capable of seamlessly entering practice after training. We observed that our traditional 3-day-cycle surgical pathology cycle (day 1-grossing; day 2 -biopsies/frozens/preview; day 3 - sign-out) consistently and negatively impacted resident education by reducing preview time, case follow-up, immunohistochemical stain (IHC) interpretation, and molecular study integration. We aimed to create a modern surgical pathology rotation that improved performance and outcomes.