Developing a Novel Interactive Colorectal Cancer Educational Session for First-Year Medical Students to Enhance Interest in Public Health.

Medical students need more exposure to and a greater understanding of their role in public health throughout their training, which may influence more of them to pursue careers in public health or change how they practice medicine in the future. A novel colorectal cancer education session was created for first year medical students to attempt to increase public health interest, improve colorectal cancer knowledge and discuss barriers to colorectal cancer screening. We constructed a novel integrated interactive peer led colorectal cancer educational session of panelists with a wide range of experiences in colorectal cancer and colorectal cancer screening. The session involved a didactic component, case presentation, and group exercises followed by assembly discussion. We surveyed first-year medical students over two consecutive years to assess their interest in public health, knowledge of colorectal cancer, and perceptions of barriers to colorectal cancer screening before and after the educational session. We also evaluated student satisfaction with the session. We compared the pre- and post-survey results to assess for changes in interest, knowledge and perceptions. 74.63% of students in 2018 and 67.7% in 2019 evaluated the session as excellent or good, with knowledge regarding colorectal cancer screening markedly increased after the educational session. Students reported knowledge and access to healthcare among the biggest patient barriers to colorectal cancer screening. Interest in public health increased by 7.5% and 5.6% in 2018 and 2019, respectively. The implementation of this interactive educational peer led exercise can increase interest in public health, improve knowledge of colorectal cancer prevention and facilitate discussions of colorectal cancer screening barriers. We hope to encourage other programs to adopt this preliminary model.

Quantifying the Subharmonic Mucosal Wave in Excised Larynges via Digital Kymography.

OBJECTIVES: In this paper, a nonlinear least squares fitting method was proposed to quantify subharmonic mucosal waves. STUDY DESIGN AND METHODS: Subharmonic mucosal waves from 10 excised canine larynges were recorded using digital kymography and analyzed using nonlinear least squares and linear least squares methods. Amplitudes of fundamental and subharmonic mucosal wave components of right-upper, right-lower, left-upper, and left-lower vocal fold lips were calculated. Lastly, phase differences of fundamental and subharmonic components of the left and right vocal folds were compared. RESULTS: The results showed that the nonlinear least squares analysis method provides a more effective complement to the linear fitting method for subharmonic mucosal wave extraction. There was a significant difference in amplitudes between the subharmonic and the fundamental components of mucosal waves (P < 0.05). The phase differences of the fundamental and the subharmonic components of the right and left vocal folds were not significantly different. CONCLUSIONS: The application of the nonlinear least squares analysis method in digital kymography is useful for the characterization of subharmonic mucosal waves.

A Case Report: Hybrid Treatment Approach to Lipoid Proteinosis of the Larynx.

OBJECTIVE: Previous research on treatment of lipoid proteinosis has focused on genetic etiology and clinical diagnosis of this rare laryngeal disorder. However, few studies on this disorder have examined treatment methods directed at improving voice qualities. The purpose of this study was to examine a novel hybrid treatment approach and its improvements in the patient's voice quality. STUDY DESIGN AND METHODS: In this case study, a 27-year-old man complaining of hoarseness of voice and effortful phonation was diagnosed with lipoid proteinosis. He was then prescribed a combination of surgical intervention and voice therapy to improve his overall voice quality. RESULTS: The results of the posttreatment evaluation demonstrate significant improvements in both objective and subjective voice quality measurements. CONCLUSION: A further examination of this hybrid approach in the treatment of lipoid proteinosis is warranted to determine its efficacy.

An Excised Canine Model of Anterior Glottic Web and Its Acoustic, Aerodynamic, and High-speed Measurements.

OBJECTIVES: This study aims to build an excised anterior glottic web (AGW) model and study the basic voice-related mechanisms of the AGW through investigating the acoustic, aerodynamic, and vibratory properties. STUDY DESIGN AND METHODS: Overall, four conditions were tested for each of the eight canine larynges used. At baseline, 10%, 20%, and 33% occlusion (as determined by the placement of the suture), acoustic, aerodynamic, and high-speed video data were collected while each larynx was phonated in a soundproof booth. RESULTS: The phonation threshold pressure (PTP) and the phonation threshold flow significantly increased as percent occlusion increased (P < 0.001). There were significant increases in jitter % and shimmer % from baseline group to AGW model groups at PTP, 1.25 PTP, and 1.5 PTP (P = 0.039, P < 0.001, P < 0.001, P < 0.001, P < 0.001, and P = 0.001, respectively). The fundamental frequency significantly increased as percent occlusion increased at all given pressures (P < 0.001). Correlation dimension (D2) was significantly higher in the AGW model groups than in the baseline group at PTP, 1.25 PTP, and 1.5 PTP (P = 0.002, P < 0.001, P = 0.01, respectively). High-speed videos revealed that, the left phase shift in the AGW model groups compared with the baseline at 1.25 PTP was significant (P = 0.027) and right phase shift at 1.5 PTP (P < 0.001). CONCLUSIONS: We presented an anatomically similar model of a type 1 AGW and confirmed its validity through aerodynamic, acoustic, and high-speed video analysis in our study. We observed and investigated the glottic web movement, which may be a new explanation for the pathologic voice-related mechanism of AGW.

Optimized Nonlinear Dynamic Analysis of Pathologic Voices With Laryngeal Paralysis Based on the Minimum Embedding Dimension.

OBJECTIVE: The present study aims to compare the correlation dimension and second-order entropy at the minimum embedding dimension with the correlation dimension (D2) and second-order entropy (K2) based on their efficiency and accuracy in differentiating between normal and pathologic voices. METHODS: The minimum embedding dimension was estimated with the Cao method. Nonlinear dynamic parameters, such as correlation dimension and second-order entropy, were used to quantitatively analyze the normal and pathologic voice samples. RESULTS: The computing time of the correlation dimension and second-order entropy at the minimum embedding dimension was reduced to approximately one third of that of traditional D2 and K2 calculations, reflecting higher efficiency. The statistical results of linear fitting suggested that the correlation dimension was highly correlated to the correlation dimension at the minimum embedding dimension, and second-order entropy calculation was highly correlated to the second-order entropy at the minimum embedding dimension. Lastly, the results of statistical comparison proved that the correlation dimension at the minimum embedding dimension and second-order entropy at the minimum embedding dimension were able to significantly differentiate between normal and disordered voices (P <0.001). CONCLUSIONS: The results suggest that the correlation dimension and second-order entropy at the minimum embedding dimension are valid analysis tools for the diagnosis of voice disorders. Additionally, the efficiency and accuracy of these parameters yield potential for clinical usage because of lower computation time than current methods.

Using Rate of Divergence as an Objective Measure to Differentiate between Voice Signal Types Based on the Amount of Disorder in the Signal.

OBJECTIVE/HYPOTHESIS: The purpose of this paper is to introduce the rate of divergence as an objective measure to differentiate between the four voice types based on the amount of disorder present in a signal. We hypothesized that rate of divergence would provide an objective measure that can quantify all four voice types. STUDY DESIGN: A total of 150 acoustic voice recordings were randomly selected and analyzed using traditional perturbation, nonlinear, and rate of divergence analysis methods. METHODS: We developed a new parameter, rate of divergence, which uses a modified version of Wolf's algorithm for calculating Lyapunov exponents of a system. The outcome of this calculation is not a Lyapunov exponent, but rather a description of the divergence of two nearby data points for the next three points in the time series, followed in three time-delayed embedding dimensions. This measure was compared to currently existing perturbation and nonlinear dynamic methods of distinguishing between voice signals. RESULTS: There was a direct relationship between voice type and rate of divergence. This calculation is especially effective at differentiating between type 3 and type 4 voices (P < 0.001) and is equally effective at differentiating type 1, type 2, and type 3 signals as currently existing methods. CONCLUSION: The rate of divergence calculation introduced is an objective measure that can be used to distinguish between all four voice types based on the amount of disorder present, leading to quicker and more accurate voice typing as well as an improved understanding of the nonlinear dynamics involved in phonation.

An Objective Parameter for Quantifying the Turbulent Noise Portion of Voice Signals.

OBJECTIVES: Currently, there are no objective measures capable of distinguishing between all four voice signal types proposed by Titze in 1995 and updated by Sprecher in 2010. We propose an objective metric that distinguishes between voice signal types based on the aperiodicity present in a signal. STUDY DESIGN: One hundred fifty voice signal samples were randomly selected from the Disordered Voice Database and subjectively sorted into the appropriate voice signal category on the basis of the classification scheme presented in Sprecher 2010. METHODS: Short-time Fourier transform was applied to each voice sample to produce a spectrum for each signal. The spectrum of each signal was divided into 250 time segments. Next, these segments were compared to each other and used to calculate an outcome named spectrum convergence ratio (SCR). Finally, the mean SCR was calculated for each of the four voice signal types. RESULTS: SCR was capable of significantly differentiating between each of the four voice signal types (P < 0.001). Additionally, this new parameter proved equally as effective at distinguishing between voice signal types as currently available parameters. CONCLUSION: SCR was capable of objectively distinguishing between all four voice signal types. This metric could be used by clinicians to quickly and efficiently diagnose voice disorders and monitor improvements in voice acoustical signals during treatment methods.

Vibratory Dynamics of Four Types of Excised Larynx Phonations.

OBJECTIVES: There are four types of signals that are typical representations of vocal fold vibratory patterns. Type 1 signals are nearly periodic, type 2 signals contain subharmonic properties, type 3 signals are chaotic, and type 4 signals are characterized as white noise. High-speed imaging allows detailed observation of these vocal fold vibratory patterns. Therefore, high-speed imaging can explore the vibratory mechanism behind each of the four types of signals. METHODS: The glottal area time series of the four types of vocal fold vibrations were calculated from high-speed images of 10 excised canine larynges. Nonlinear dynamic parameters of correlation dimension (D2) and Kolmogorov entropy (K2) were used to quantify the characteristics of the glottal areas and acoustical signals for each voice signal type. RESULTS: The correlation dimension and Kolmogorov entropy of the glottal areas and acoustical signals for type 1, 2, and 3 voice signals were consistent with the results of previous studies. Interestingly, there was a difference between the glottal area and acoustical signals of type 4 voice signals (P < 0.001). Both the correlation dimension and Kolmogorov entropy of the type 4 glottal area were close to 0. In contrast, the type 4 acoustical signals had an infinite correlation dimension and a Kolmogorov entropy that was close to 1. CONCLUSIONS: Turbulence in the vocal tract creates high-frequency breathiness, causing noise in the acoustical signal of type 4 voice, proving that the acoustical signal does not represent the motion mechanism behind type 4 voice. The results of this study demonstrate that high-speed imaging can provide a more accurate representation of the type 4 vocal fold vibratory pattern, and a more effective method to explore the mechanism of type 4 signals.