Introduction: There is a significant overlap between paediatrics and otolaryngology relating to clinical practice of the two specialties. A lack of cross-training has been identified in previous studies, but the specifics have not been established. The present study was directed at paediatricians in Canada, and examined the need for mandatory otolaryngology training during paediatric residency.
Methods: Surveys were mailed out to paediatricians in Canada who had completed residency within the past 20 years. Guidelines for the mailing procedure were regulated by the Royal College of Physicians and Surgeons of Canada. A cover letter, survey form and return envelope were included in the package. Data were tabulated and described using descriptive statistics.
Results: Six hundred sixty-six surveys were mailed; the response rate was 48%. Seventy-three per cent of paediatricians indicated that otolaryngology training should be mandatory during paediatric residency. Seventy-nine per cent of general paediatricians and 68% of subspecialists also believed that it should be mandatory training. Seventy per cent of paediatricians indicated that clinical experience was the best format for otolaryngology training, the other options being lectures or rotations. Postgraduate year 2 was the most preferred year for this training. For paediatricians who indicated mandatory training, 45% indicated that it could not replace something else, 38% said that it could replace another experience and the remainder were undecided. The respondents provided helpful commentary.
Interpretation: The majority of surveyed paediatricians in Canada believe that otolaryngology training should be mandatory during paediatric residency. There was also a general consensus relating to the format (clinical experience) and the duration (two to four weeks) of the training.
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http://dx.doi.org/10.1093/pch/13.6.493 | DOI Listing |
Clin Otolaryngol
December 2024
Department of Otolaryngology and Head and Neck Surgery, Waikato Hospital, Hamilton, New Zealand.
Am J Otolaryngol
December 2024
Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin 300192, China; Institute of Otolaryngology of Tianjin, Tianjin, China; Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China; Key Clinical Discipline of Tianjin (Otolaryngology), Tianjin, China; Otolaryngology Clinical Quality Control Centre, Tianjin, China.
Purpose: To use deep learning technology to design and implement a model that can automatically classify laryngoscope images and assist doctors in diagnosing laryngeal diseases.
Materials And Methods: The experiment was based on 3057 images (normal, glottic cancer, granuloma, Reinke's Edema, vocal cord cyst, leukoplakia, nodules and polyps) from the dataset Laryngoscope8. A classification model based on deep neural networks was developed and tested.
Am J Otolaryngol
December 2024
Emory Winship Cancer Institute, Atlanta, GA, United States of America.
Background: Due to its complexity and multimodality treatment needs, traditional delivery of head and neck cancer care often occurs in a multidisciplinary cancer center, frequently in a university-based program in an urban setting. Fellowship training opportunities for subspecialty-focused head and neck surgeons have increased over recent years. There is a persistent concern that the number of newly minted Head & Neck Surgeons graduating each year outpaces the number of university-based employment opportunities, and that the workforce does not match the job opportunities.
View Article and Find Full Text PDFJ Pers Med
December 2024
Division of Neurotology and Skull Base Surgery, Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, CA 92697, USA.
This study aimed to develop a machine learning (ML) algorithm that can predict unplanned reoperations and surgical/medical complications after vestibular schwannoma (VS) surgery. All pre- and peri-operative variables available in the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database (n = 110), except those directly related to our outcome variables, were used as input variables. A deep neural network model consisting of seven layers was developed using the Keras open-source library, with a 70:30 breakdown for training and testing.
View Article and Find Full Text PDFAudiol Res
December 2024
Starkey Hearing, Eden Prairie, MN 55344, USA.
Despite the significant advancements in hearing aid technology, their adoption rates remain low, with stigma continuing to be a major barrier for many. This review aims to assess the origins and current state of hearing aid stigma, as well as explore potential strategies for alleviating it. This review examines the societal perceptions, psychological impacts, and recent technological advancements that can influence hearing aid adoption and reduce stigma.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!