Diagnostic Yield of Computed Tomography for Cancer Detection in a Tertiary Referral Population of Idiopathic Inflammatory Myositis Patients.

Objective: To inform guidance for cancer detection in patients with idiopathic inflammatory myopathy (IIM), we evaluated the diagnostic yield of computed tomography (CT) imaging for cancer screening/surveillance within distinct IIM subtypes and myositis-specific autoantibody strata.

Methods: We conducted a single-center, retrospective cohort study in IIM patients. Overall diagnostic yield (number of cancers diagnosed/number of tests performed), percentage of false positives (number of biopsies performed not leading to cancer diagnosis/number of tests performed), and test characteristics were determined on CT of the chest and abdomen/pelvis.

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.

Physician Use of Stigmatizing Language in Patient Medical Records.

Importance: Negative attitudes toward patients can adversely impact health care quality and contribute to health disparities. Stigmatizing language written in a patient's medical record can perpetuate negative attitudes and influence decision-making of clinicians subsequently caring for that patient.

Objective: To identify and describe physician language in patient health records that may reflect, or engender in others, negative and positive attitudes toward the patient.

Testimonial Injustice: Linguistic Bias in the Medical Records of Black Patients and Women.

Background: Black Americans and women report feeling doubted or dismissed by health professionals.

Objective: To identify linguistic mechanisms by which physicians communicate disbelief of patients in medical records and then to explore racial and gender differences in the use of such language.

Design: Cross-sectional.

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.

BeeSpace Navigator: exploratory analysis of gene function using semantic indexing of biological literature.

With the rapid decrease in cost of genome sequencing, the classification of gene function is becoming a primary problem. Such classification has been performed by human curators who read biological literature to extract evidence. BeeSpace Navigator is a prototype software for exploratory analysis of gene function using biological literature.

Identifying overrepresented concepts in gene lists from literature: a statistical approach based on Poisson mixture model.

Background: Large-scale genomic studies often identify large gene lists, for example, the genes sharing the same expression patterns. The interpretation of these gene lists is generally achieved by extracting concepts overrepresented in the gene lists. This analysis often depends on manual annotation of genes based on controlled vocabularies, in particular, Gene Ontology (GO).

Measuring population health using personal health messages.

Personal health messages - inter patient communications within online communities; represent a new path towards providing continuous information about patient derived health status. We apply natural language processing techniques to personal health messages from online message boards to demonstrate the ability to track trends in people's positive or negative opinion (sentiment) regarding particular drugs over time. The significant changes in sentiment correspond to FDA announcements and other publicity.