Objectives/Hypothesis: This scoping review aims to map out existing disparities research within the subspecialty of laryngology in order to highlight gaps in knowledge and guide future research.Study Design: Scoping Review.Methods: We completed a scoping review of PubMed, Ovid Embase, and the Cochrane Library for primary research focused on evaluating the existence and impact of disparities in race/ethnicity, sex/gender, insurance status, education level, income, geography, and LGBTQ identity in the context of various laryngological conditions. Publications of any design and date, performed in the United States, and focusing on the adult population exclusively were included.Results: Of the 4,999 unique abstracts identified, 51 articles were ultimately included. The most frequently examined condition in relation to disparities was laryngeal cancer (27 of 51), followed by voice disorders (15 of 51), deglutitive disorders (eight of 51), and airway disorders (one of 51). Sources of inequity evaluated from most common to least common were race/ethnicity (43 of 51), sex/gender (39 of 51), insurance status (23 of 51), geography (23 of 51), income (21 of 51), and education level (16 of 51). No study examined the association of LGBTQ identity with inequity.Conclusions: This scoping review highlights the limited extent of disparities research in laryngology and establishes the need for further scholarship on the impact of disparities in laryngology care. The pathologies studied were, in decreasing order of frequency: laryngeal cancer, voice disorders, deglutitive disorders, and airway disorders. Race/ethnicity and sex/gender were the most common disparities examined, with no evaluation of LGBTQ-related care inequity.
Objectives/Hypothesis This scoping review aims to provide a broad overview of the applications of artificial intelligence (AI) to office laryngoscopy to identify gaps in knowledge and guide future research. Study Design Scoping Review. Methods Searches for studies on AI and office laryngoscopy were conducted in five databases. Title and abstract and then full‐text screening were performed. Primary research studies published in English of any date were included. Studies were summarized by: AI applications, targeted conditions, imaging modalities, author affiliations, and dataset characteristics. Results Studies focused on vocal fold vibration analysis (43%), lesion recognition (24%), and vocal fold movement determination (19%). The most frequently automated tasks were recognition of vocal fold nodules (19%), polyp (14%), paralysis (11%), paresis (8%), and cyst (7%). Imaging modalities included high‐speed laryngeal videos (45%), stroboscopy (29%), and narrow band imaging endoscopy (7%). The body of literature was primarily authored by science, technology, engineering, and math (STEM) specialists (76%) with only 30 studies (31%) involving co‐authorship by STEM specialists and otolaryngologists. Datasets were mostly from single institution (84%) and most commonly originated from Germany (23%), USA (16%), Spain (9%), Italy (8%), and China (8%). Demographic information was only reported in 39 studies (40%), with age and sex being the most commonly reported, whereas race/ethnicity and gender were not reported in any studies. Conclusion More interdisciplinary collaboration between STEM and otolaryngology research teams improved demographic reporting especially of race and ethnicity to ensure broad representation, and larger and more geographically diverse datasets will be crucial to future research on AI in office laryngoscopy. Level of Evidence NA Laryngoscope, 132:1993–2016, 2022
Background: The COVID-19 pandemic has necessitated a rapid uptake of telemedicine in primary care requiring both patients and providers to learn how to navigate care remotely. This change can impact the patient–provider relationship that often defines care, especially in primary care. Objective: This study aims to provide insight into the experiences of patients and providers with telemedicine during the pandemic, and the impact it had on their relationship. Research Design: A qualitative study using thematic analysis of semistructured interviews. Subjects: Primary care providers (n=21) and adult patients (n=65) with chronic disease across primary care practices in 3 National Patient-centered Clinical Research Network sites in New York City, North Carolina, and Florida. Measures: Experiences with telemedicine during the COVID-19 pandemic in primary care. Codes related to the patient–provider relationship were analyzed for this study. Results: A recurrent theme was the challenge telemedicine posed on rapport building and alliance. Patients felt that telemedicine affected provider’s attentiveness in varying ways, whereas providers appreciated that telemedicine provided unique insight into patients’ lives and living situations. Finally, both patients and providers described communication challenges. Conclusions: Telemedicine has altered structure and process aspects of primary health care such as the physical spaces of encounters, creating a new setting to which both patients and providers must adjust. It is important to recognize the opportunities and limits that this new technology has to help providers maintain the type of one-on-one attention that patients expect and that contributes to relationship building.
Introduction Many health providers and communicators who are concerned that patients will not understand numbers instead use verbal probabilities (e.g., terms such as “rare” or “common”) to convey the gist of a health message. Objective To assess patient interpretation of and preferences for verbal probability information in health contexts. Methods We conducted a systematic review of literature published through September 2020. Original studies conducted in English with samples representative of lay populations were included if they assessed health-related information and elicited either (a) numerical estimates of verbal probability terms or (b) preferences for verbal vs. quantitative risk information. Results We identified 33 original studies that referenced 145 verbal probability terms, 45 of which were included in at least two studies and 19 in three or more. Numerical interpretations of each verbal term were extremely variable. For example, average interpretations of the term “rare” ranged from 7 to 21%, and for “common,” the range was 34 to 71%. In a subset of 9 studies, lay estimates of verbal probability terms were far higher than the standard interpretations established by the European Commission for drug labels. In 10 of 12 samples where preferences were elicited, most participants preferred numerical information, alone or in combination with verbal labels. Conclusion Numerical interpretation of verbal probabilities is extremely variable and does not correspond well to the numerical probabilities established by expert panels. Most patients appear to prefer quantitative risk information, alone or in combination with verbal labels. Health professionals should be aware that avoiding numeric information to describe risks may not match patient preferences, and that patients interpret verbal risk terms in a highly variable way.
Objectives/Hypothesis A limited number of three‐dimensionally (3D)‐printed laryngeal simulators have been described in the literature, only one of which is specifically designed for percutaneous injection laryngoplasty (PIL) training and is currently of limited availability. This study describes the development and evaluation of a high‐fidelity, open‐source, low‐cost 3D‐printed simulator for PIL training, improving on existing models. Study Design Simulator design and survey evaluation. Methods Computed tomography scans of the upper airways were processed with 3D Slicer to generate a computer model of the endolarynx. Blender and Fusion 360 were used to refine the mucosal model and develop casts for silicone injection molding. The casted endolaryngeal structures were inserted into a modified version of a publicly available laryngeal cartilage model. The final models were evaluated by 10 expert laryngologists using a customized version of the Michigan Standard Simulation Experience Scale. Internal consistency and interrater reliability of the survey were evaluated using Cronbach's α and intraclass correlation, respectively. Results Expert laryngologists highly rated the model for measures of fidelity, educational value, and overall quality (mean = 4.8, standard deviation = 0.5; 1 = strongly disagree, 5 = strongly agree). All reviewers rated the model as ready for use as is or with slight modifications. The filament needed for one cartilage model costs $0.96, whereas the silicone needed for one soft‐tissue model costs $1.89. Conclusions Using 3D‐printing technology, we successfully created the first open‐source, low‐cost, and anatomically accurate laryngeal model for injection laryngoplasty training. Our simulator is made freely available for download on Wikifactory with step‐by‐step tutorials for 3D printing, silicone molding, assembly, and use. Level of Evidence NA Laryngoscope, 131:E890–E895, 2021
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