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. Demographic data about applicants and letter writers were collected. Linguistic analysis was performed using a text analysis software, and results were evaluated with nonparametric tests, multiple linear regression models, and a mixed effects regression model. RESULTS: Letters of recommendation were analyzed; 720 letters for 188 (23.4%) female applicants and 2,337 letters for 616 (76.6%) male applicants. Compared with LORs written for men, those written for women had more references to categories of anxiety (eg, worried and fearful) and affiliation (eg, ally and friend). Letters for women had more "clout." In subgroup analysis, letters for female plastic surgery applicants had more words signaling power, whereas recommendations for female applicants from orthopedic residencies had more words related to anxiety, achievement, work, and leisure. CONCLUSIONS: Letters of recommendation written for female residents applying to hand fellowship had more references to anxiety but were written with higher clout and more words of affiliation. Subgroup analysis looking at orthopedic and plastic surgery applicants separately found a mixed picture. Overall, these LORs written for applicants to hand fellowship had no notable specific patterns of gender bias in our analyses. CLINICAL RELEVANCE: Because programs look to train the next generation of hand surgeons, alerting letter readers to trends in implicit bias may help in the selection of qualified applicants. Bringing topics of implicit bias forward may help writers think more critically about word choice and topics.

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 in silico read data are modular, scalable, and reflect user-defined community profiles, while the downstream analysis is done using a variety of metagenomic classifiers. Reported results include information on resource utilization, time-to-answer, and performance. Real-world data can also be analyzed using selected classifiers and results benchmarked against simulations. To test the utility of the META software, simulated data was compared to real-world viral and bacterial metagenomic samples run on four different sequencers and analyzed using 12 metagenomic classifiers. Lastly, we introduce "META Score": a unified, quantitative value which rates an analytic classifier's ability to both identify and count taxa in a representative sample.

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. QUESTIONS/PURPOSES: (1) Are there differences in word count and word categories in letters of recommendation describing women and men applicants, regardless of author gender? (2) Is author gender associated with word category differences in letters of recommendation? (3) Do authors of different academic rank use different words to describe women versus men applicants? METHODS: Using a linguistic analysis in a retrospective study, we analyzed all letters of recommendation (2834 letters) written for all 738 applicants with completed Electronic Residency Application Service applications submitted to the Johns Hopkins Orthopaedic Surgery Residency program during the 2018 to 2019 cycle to determine differences in word category use among applicants by gender, authors by gender, and authors by academic rank. Thirty nine validated word categories from the Linguistic Inquiry and Word Count dictionary along with seven additional word categories from previous publications were used in this analysis. The occurrence of words in each word category was divided by the number of words in the letter to obtain a word frequency for each letter. We calculated the mean word category frequency across all letters and analyzed means using non-parametric tests. For comparison of two groups, a p value threshold of 0.05 was used. For comparison of multiple groups, the Bonferroni correction was used to calculate an adjusted p value (p = 0.00058). RESULTS: Letters of recommendation for women applicants were slightly longer compared with those for men applicants (366 ± 188 versus 339 ± 199 words; p = 0.003). When comparing word category differences by applicant gender, letters for women applicants had slightly more "achieve" words (0.036 ± 0.015 versus 0.035 ± 0.018; p < 0.0001). Letters for men had more use of their first name (0.016 ± 0.013 versus 0.014 ± 0.009; p < 0.0001), and more "young" words (0.001 ± 0.003 versus 0.000 ± 0.001; p < 0.0001) than letters for women applicants. These differences were very small as each 0.001 difference in mean word frequency was equivalent to one more additional word from the word category appearing when comparing three letters for women to three letters for men. For differences in letters by author gender, there were no word category differences between men and women authors. Finally, when looking at author academic rank, letters for men applicants written by professors had slightly more "research" terms (0.011 ± 0.010) than letters written by associate professors (0.010 ± 0.010) or faculty of other rank (0.009 ± 0.011; p < 0.0001), a finding not observed in letters written for women. CONCLUSIONS: Although there were some minor differences favoring women, language in letters of recommendation to an academic orthopaedic surgery residency program were overall similar between men and women applicants. CLINICAL RELEVANCE: Given the similarity in language between men and women applicants, increasing women applicants may be a more important factor in addressing the gender gap in orthopaedics.

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. However, safety problems have resulted from the lack of post marketing surveillance information about drugs, with underreporting rates of up to 98% within such systems. We explore the use of online health forums as a source of data to identify drugs for further FDA scrutiny. In this work we aggregate individuals' opinions and review of drugs similar to crowd intelligence3. We use natural language processing to group drugs discussed in similar ways and are able to successfully identify drugs withdrawn from the market based on messages discussing them before their removal.