Background The residency match process for competitive specialties hinders programs' ability to holistically review applications. Objective A computer simulation model of the residency application process was created to test the hypotheses that (1) it is advantageous to medical students to apply to the maximum number of programs under the current system, and (2) including a medical student's residency program preferences at the beginning of the application process improves the efficiency of the system for applicants and programs as quantified by the number of interview invitations received. Methods The study was conducted in 2016 using 2014 Otolaryngology Match data. A computer model was created to perform simulations for multiple scenarios to test the hypotheses. Students were assigned scores representing easy and hard metrics and program preferences, simulating a mixture of individual student preference and general program popularity. Results We modeled a system of 99 otolaryngology residency programs with 292 residency spots and 460 student applicants. While it was individually advantageous for an applicant to apply to the maximum number of programs, this led to a poor result for the majority of students when all applicants undertook the strategy. The number of interview invitations improved for most applicants when preference was revealed. Conclusions Offering applicants an option to provide program preference improves the practical number of interview invitations. This enables programs to review applicants holistically-instead of using single parameters such as United States Medical Licensing Examination scores-which facilitates a selection of applicants who will be successful in residency.

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Objective. This study seeks to quantify how current speech recognition systems perform on dysphonic input and if they can be improved.Study Design. Experimental machine learning methods based on a retrospective database. Setting. Single academic voice center.Methods. A database of dysphonic speech recordings was created and tested against 3 speech recognition platforms. Platform performance on dysphonic voice input was compared to platform performance on normal voice input. A custom speech recognition model was trained on voice from patients with spasmodic dysphonia or vocal cord paralysis. Custom model performance was compared to base model performance.Results. All platforms performed well on normal voice, and 2 platforms performed significantly worse on dysphonic speech. Accuracy metrics on dysphonic speech returned values of 84.55%, 88.57%, and 93.56% for International Business Machines (IBM) Watson, Amazon Transcribe, and Microsoft Azure, respectively. The secondary analysis demonstrated that the lower performance of IBM Watson and Amazon Transcribe was driven by performance on spasmodic dysphonia and vocal fold paralysis. Thus, a custom model was built to increase the accuracy of these pathologies on the Microsoft platform. Overall, the performance of the custom model on dysphonic voices was 96.43% and on normal voices was 97.62%. Conclusion.Current speech recognition systems generally perform worse on dysphonic speech than on normal speech. We theorize that poor performance is a consequence of a lack of dysphonic voices in each platform's original training dataset. We address this limitation with transfer learning used to increase the performance of these systems on all dysphonic speech.

Objective. Defining a clinician's ability to perceptually identify mass from voice will inform the feasibility, design priorities, and performance standards for tools developed to screen for laryngeal mass from voice. This study defined clinician ability of and examined the impact of expertise on screening for laryngeal mass from voice.Study Design. Task comparison study between experts and nonexperts rating voices for the probability of a laryngeal mass.Setting. Online, remote.Methods. Experts (voice-focused speech-language pathologists and otolaryngologists) and nonexperts (general medicine providers) rated 5-s/i/voice samples (with pathology defined by laryngoscopy) for the probability of laryngeal mass via an online survey. The intraclass correlation coefficient (ICC) estimated interrater and intrarater reliability. Diagnostic performance metrics were calculated. A linear mixed effects model examined the impact of expertise and pathology on ratings.Results. Forty clinicians (21 experts and 19 nonexperts) evaluated 344 voice samples. Experts outperformed nonexperts, with a higher area under the curve (70% vs 61%), sensitivity (49% vs 36%), and specificity (83% vs 77%) (all comparisons p < .05). Interrater reliability was fair for experts and poor for nonexperts (ICC: 0.48 vs 0.34), while intrarater reliability was excellent and good, respectively (ICC: 0.9 and 0.6). The main effects of expertise and underlying pathology were significant in the linear model (p < .001). Conclusion.Clinicians demonstrate inadequate performance screening for laryngeal mass from voice to use auditory perception for dysphonia triage. Experts' superior performance indicates that there is acoustic information in a voice that may be utilized to detect laryngeal mass based on voice.