A Comparison of Web-Based Cancer Risk Calculators That Inform Shared Decision-making for Lung Cancer Screening.

INTRODUCTION: To align patient preferences and understanding with harm-benefit perception, the Centers for Medicare & Medicaid Services (CMS) mandates that providers engage patients in a collaborative shared decision-making (SDM) visit before LDCT. Nonetheless, patients and providers often turn instead to the web for help making decisions. Several web-based lung cancer risk calculators (LCRCs) provide risk predictions and screening recommendations; however, the accuracy, consistency, and subsequent user interpretation of these predictions between LCRCs is ambiguous. We conducted a systematic review to assess this variability. DESIGN: Through a systematic Internet search, we identified 10 publicly available LCRCs and categorized their input variables: demographic factors, cancer history, smoking status, and personal/environmental factors. To assess variance in LCRC risk prediction outputs, we developed 16 hypothetical patients along a risk continuum, illustrated by randomly assigned input variables, and individually compared them to each LCRC against the empirically validated "gold-standard" PLCO risk model in order to evaluate the accuracy of the LCRCs within identical time-windows. RESULTS: From the inclusion criteria, 11 calculators were initially identified. The analyzed calculators also vary in output characteristics and risk depiction for hypothetical patients. There were 13 total instances across ten hypothetical patients in which the sample standard error exceeded the mean risk percentage across all general samples and set standard calculations. The largest measured difference is 16.49% for patient 8, and the smallest difference is 0.01% for patient 2. The largest measured difference is 16.49% for patient 8, and the smallest difference is 0.01% for patient 2. CONCLUSION: Substantial variability in the depiction of lung cancer risk for hypothetical patients exists across the web-based LCRCs due to their respective inputs and risk prediction models. To foster informed decision-making in the SDM-LDCT context, the input variables, risk prediction models, risk depiction, and screening recommendations must be standardized to best practice.

3D-Printed Synthetic Vocal Fold Models.

OBJECTIVE: Synthetic vocal fold (VF) models used for studying the physics of voice production are comprised of silicone and fabricated using traditional casting processes. The purpose of this study was to develop and demonstrate a new method of creating synthetic VF models through 3D printing in order to reduce model fabrication time, increase yield, and lay the foundation for future models with more life-like geometric, material, and vibratory properties. STUDY DESIGN: Basic science. METHODS: A 3D printing technique based on embedding a UV-curable liquid silicone into a gel-like medium was selected and refined. Cubes were printed and subjected to tensile testing to characterize their material properties. Self-oscillating VF models were then printed, coated with a thin layer of silicone representing the epithelium, and used in phonation tests to gather onset pressure, frequency, and amplitude data. RESULTS: The cubes were found to be anisotropic, exhibiting different modulus values depending on the orientation of the printed layers. The VF models self-oscillated and withstood the strains induced by phonation. Print parameters were found to affect model vibration frequency and onset pressure. Primarily due to the design of the VF models, their onset pressures were higher than what is found in human VFs. However, their frequencies were within a comparable range. CONCLUSION: The results demonstrate the ability to 3D print synthetic, self-oscillating VF models. It is anticipated that this method will be further refined and used in future studies exploring flow-induced vibratory characteristics of phonation.

Quality of Deaf and Hard-of-Hearing Mobile Apps: Evaluation Using the Mobile App Rating Scale (MARS) With Additional Criteria From a Content Expert.

BACKGROUND: The spread of technology and dissemination of knowledge across the World Wide Web has prompted the development of apps for American Sign Language (ASL) translation, interpretation, and syntax recognition. There is limited literature regarding the quality, effectiveness, and appropriateness of mobile health (mHealth) apps for the deaf and hard-of-hearing (DHOH) that pose to aid the DHOH in their everyday communication and activities. Other than the star-rating system with minimal comments regarding quality, the evaluation metrics used to rate mobile apps are commonly subjective. OBJECTIVE: This study aimed to evaluate the quality and effectiveness of DHOH apps using a standardized scale. In addition, it also aimed to identify content-specific criteria to improve the evaluation process by using a content expert, and to use the content expert to more accurately evaluate apps and features supporting the DHOH. METHODS: A list of potential apps for evaluation was generated after a preliminary screening for apps related to the DHOH. Inclusion and exclusion criteria were developed to refine the master list of apps. The study modified a standardized rating scale with additional content-specific criteria applicable to the DHOH population for app evaluation. This was accomplished by including a DHOH content expert in the design of content-specific criteria. RESULTS: The results indicate a clear distinction in Mobile App Rating Scale (MARS) scores among apps within the study's three app categories: ASL translators (highest score=3.72), speech-to-text (highest score=3.6), and hard-of-hearing assistants (highest score=3.90). Of the 217 apps obtained from the search criteria, 21 apps met the inclusion and exclusion criteria. Furthermore, the limited consideration for measures specific to the target population along with a high app turnover rate suggests opportunities for improved app effectiveness and evaluation. CONCLUSIONS: As more mHealth apps enter the market for the DHOH population, more criteria-based evaluation is needed to ensure the safety and appropriateness of the apps for the intended users. Evaluation of population-specific mHealth apps can benefit from content-specific measurement criteria developed by a content expert in the field.