Evaluating Language Characteristics and Related Gender Bias in Letters of Recommendation for Hand Surgery Fellowship.

Purpose: Letters of recommendation (LORs) function as an indicator of competence and future potential for a trainee. Our purpose was to evaluate gender bias in hand surgery fellowship applicant LORs.

Methods: This was a retrospective study of all LORs submitted to a hand surgery fellowship program between 2015 and 2020.

The META tool optimizes metagenomic analyses across sequencing platforms and classifiers.

A major challenge in the field of metagenomics is the selection of the correct combination of sequencing platform and downstream metagenomic analysis algorithm, or "classifier". Here, we present the Metagenomic Evaluation Tool Analyzer (META), which produces simulated data and facilitates platform and algorithm selection for any given metagenomic use case. META-generated read data are modular, scalable, and reflect user-defined community profiles, while the downstream analysis is done using a variety of metagenomic classifiers.

Are There Gender-based Differences in Language in Letters of Recommendation to an Orthopaedic Surgery Residency Program?

Background: Letters of recommendation are considered one of the most important factors for whether an applicant is selected for an interview for orthopaedic surgery residency programs. Language differences in letters describing men versus women candidates may create differential perceptions by gender. Given the gender imbalance in orthopaedic surgery, we sought to determine whether there are differences in the language of letters of recommendation by applicant gender.

Predicting adverse drug events from personal health messages.

Adverse drug events (ADEs) remain a large problem in the United States, being the fourth leading cause of death, despite post market drug surveillance. Much post consumer drug surveillance relies on self-reported "spontaneous" patient data. Previous work has performed datamining over the FDA's Adverse Event Reporting System (AERS) and other spontaneous reporting systems to identify drug interactions and drugs correlated with high rates of serious adverse events.