OBJECTIVE Epilepsy impacts 470,000 children in the United States. For patients with drug-resistant epilepsy (DRE) and unresectable seizure foci, vagus nerve stimulation (VNS) is a treatment option. Predicting response to VNS has been historically challenging. The objective of this study was to create a clinical VNS prediction tool for use in an outpatient setting. METHODS The authors performed an 11-year retrospective cohort analysis with 1-year follow-up. Patients < 21 years of age with DRE who underwent VNS (n = 365) were included. Logistic regressions were performed to assess clinical factors associated with VNS response (≥ 50% seizure frequency reduction after 1 year); 70% and 30% of the sample were used to train and validate the multivariable model, respectively. A prediction score was subsequently developed. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. RESULTS Variables associated with VNS response were < 4-year epilepsy duration before VNS (p = 0.008) and focal motor seizures (p = 0.037). The variables included in the clinical prediction score were epilepsy duration before VNS, age at seizure onset, number of pre-VNS antiseizure medications, if VNS was the patient’s first therapeutic epilepsy surgery, and predominant seizure semiology. The final AUCs were 0.7013 for the "fitted" sample and 0.6159 for the "validation" sample. CONCLUSIONS The authors developed a clinical model to predict VNS response in a large sample of pediatric patients treated with VNS. Despite the large sample size, clinical variables alone were not able to accurately predict VNS response. This score may be useful after further validation, although its predictive ability underscores the need for more robust biomarkers to predict treatment response.

Objective As in many realms of academia and medicine, in obstetrics and gynecology, women experience gender bias in residency evaluations and academic promotions. More specifically, women in Maternal-Fetal Medicine (MFM) are underrepresented within departmental leadership positions. As a means of identifying spaces where bias may exist, multiple investigators have previously reported on gender bias in letters of recommendation (LORs) for residency and subspecialty training programs. We aimed to determine if linguistic differences exist in LORs for self-identified male and female applicants to MFM fellowship at an academic institution. Study Design This was a retrospective single-site cohort study from 2019 to 2021. Data collected included applicant's age, self-reported race/ethnicity and gender, geographic region of residency, step 1 and 2 scores, scholarly and volunteer activities, and number of LORs. The Linguistic Inquiry and Word Count (LIWC) software, a validated text analysis program, was used to characterize LOR linguistic content. Multivariable analysis was used to compare letter characteristics to applicant demographics. Results A total of 212 applications were reviewed, including 808 LORs. Women comprised 76.9% of applicants, and men 23.1%. Most applicants identified as non-Hispanic White (52.8%). Men were more likely to be international medical graduates (20 vs. 6%, p ≤ 0.01), and women reported more volunteer activities (7.1 ± 5.1 vs. 5.5 ± 4.3, p = 0.04). There were no differences in step scores, number of research projects, or number of LORs. Multivariable analysis controlling for applicant race, step 1 score, and gender of letter writer revealed that letters written for males contained significantly more references to the word category cognitive processes (7.4 ± 0.2 vs. 7.1 ± 0.1, p = 0.046), specifically in reference to the subcategories of certainty and differentiation. Conclusion We identified linguistic differences in LORs written for MFM applicants, suggesting potential bias in the style of writing for male and female physicians applying to this field. Key Points

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