Perspectives on Application and Interview Capping in Residency Selection of Surgical Subspecialties.

OBJECTIVE: With the advent of virtual interviews, the potential for interview hoarding by applicants became of greater concern due to lack of financial constraints associated with in-person interviewing. Simultaneously, the average number of applications submitted each year is rising. Currently there is no cap to the number of applications or interviews an applicant may complete when applying to residency, with the exception of ophthalmology with a cap of 15 interviews. No studies have assessed the applicants' perspectives on an application or interview cap. We assessed the attitudes of surgical subspecialty applicants towards capping, which may be useful when considering innovations in residency selection. DESIGN/SETTING/PARTICIPANTS: About 1841 applicants to the Johns Hopkins' ophthalmology, urology, plastic surgery, and orthopedic surgery residency programs from the 2022-2023 cycle were invited to respond to a 22-item questionnaire. Statistical analyses of aggregate data were conducted using R. RESULTS: Of the 776/1841 (42%) responses, 288 (40%) were in support of an application cap, while 455 (63%) were in support of an interview cap. Specialty (p < 0.001), gender (p < 0.001), taking a gap year (p = 0.02), medical school region (p = 0.04), and number of interviews accepted off of a waitlist (p = 0.01) were all significantly associated with a difference in opinion regarding an application cap. Specialty (p < 0.001), USMLE Step 1 score (p = 0.004), number of interviews (p < 0.001), and number of programs ranked (p < 0.001) were all significantly associated with a difference in opinion regarding an interview cap. Of those applicants who were in support of the respective caps they believed that on average a cap should consist of 48.1 (16.1) applications and 16.0 (8.0) interviews. CONCLUSIONS: Our findings highlight the desire for interview caps among the majority of applicants to surgical subspecialties and thus this innovation may be considered by other specialties in the era of virtual interviews.

Mortality risk prediction for primary appendiceal cancer.

BACKGROUND: Accurately predicting survival in patients with cancer is crucial for both clinical decision-making and patient counseling. The primary aim of this study was to generate the first machine-learning algorithm to predict the risk of mortality following the diagnosis of an appendiceal neoplasm. METHODS: Patients with primary appendiceal cancer in the Surveillance, Epidemiology, and End Results database from 2000 to 2019 were included. Patient demographics, tumor characteristics, and survival data were extracted from the Surveillance, Epidemiology, and End Results database. Extreme gradient boost, random forest, neural network, and logistic regression machine learning models were employed to predict 1-, 5-, and 10-year mortality. After algorithm validation, the best-performance model was used to develop a patient-specific web-based risk prediction model. RESULTS: A total of 16,579 patients were included in the study, with 13,262 in the training group (80%) and 3,317 in the validation group (20%). Extreme gradient boost exhibited the highest prediction accuracy for 1-, 5-, and 10-year mortality, with the 10-year model exhibiting the maximum area under the curve (0.909 [±0.006]) after 10-fold cross-validation. Variables that significantly influenced the predictive ability of the model were disease grade, malignant carcinoid histology, incidence of positive regional lymph nodes, number of nodes harvested, and presence of distant disease. CONCLUSION: Here, we report the development and validation of a novel prognostic prediction model for patients with appendiceal neoplasms of numerous histologic subtypes that incorporate a vast array of patient, surgical, and pathologic variables. By using machine learning, we achieved an excellent predictive accuracy that was superior to that of previous nomograms.