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Anatomical Models versus Nontactile Distanced Learning in Otolaryngology Teaching.

ABSTRACT: Introduction Medical schools in the United Kingdom are under increasing pressure to provide more streamlined, applicable teaching due to rising numbers of trainee doctors but are failing to meet their educational need for otolaryngology. The recent novel coronavirus disease 2019 (COVID-19) pandemic has placed additional pressures on medical schools to adapt the medium over which the curriculum is delivered. The use of tactile learning with three-dimensional models and distanced learning via videoconferencing may provide alternative teaching methods to meet otolaryngology undergraduate learning requirements. This pilot study aimed to assess the differences in undergraduate student attitudes toward tactile learning via nontactile distanced learning and review their acceptability among this cohort. Methods Two groups of medical students observed a single educational event on the larynx and management of the airway. The learning opportunity was delivered in a lecture format with the lecturer demonstrating on an anatomical model of the larynx. Group one (tactile group) had an identical model to interact with during the lecture and were present within the lecture theater; group two (nontactile group) did not and observed the lecture via video link. Students were asked to rank their opinion to several statements about the session based on an 11-point Likert's scale and give qualitative feedback. Results All ranked feedback was mainly positive. Tactile learning was statistically equivalent to nontactile learning based on the ranked feedback from the students, except for "improvement in anatomical knowledge," for which the students believed tactile learning was superior ( p = 0.017). A variety of qualitative feedback was received by both groups. Conclusion This pilot study provides evidence for the acceptability among students of the use of nontactile distanced learning to deliver the otolaryngology undergraduate curriculum compared with tactile learning. This can provide the basis for larger studies to assess the educational impact of these different teaching methods.

SUBMITTER: Pandya A

PROVIDER: S-EPMC8440056 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ImportancePhotographic nasal analysis constitutes a critical step along the path toward accurate diagnosis and precise surgical planning in rhinoplasty. The learned process by which one assesses photographs, analyzes relevant anatomical landmarks, and generates a global view of the nasal aesthetic is less widely described.ObjectivesTo discern the common pitfalls in performing photographic nasal analysis and to quantify the utility of a systematic approach model in teaching photographic nasal analysis to otolaryngology residents.Design, setting, and participantsThis prospective observational study included 20 participants from a university-based otolaryngology residency program. The control and intervention groups underwent baseline graded assessment of 3 patients. The intervention group received instruction on a systematic approach model for nasal analysis, and both groups underwent postintervention testing at 10 weeks. Data were collected from October 1, 2015, through June 1, 2016.InterventionA 10-minute, 11-slide presentation provided instruction on a systematic approach to nasal analysis to the intervention group.Main outcomes and measuresGraded photographic nasal analysis using a binary 18-point system.ResultsThe 20 otolaryngology residents (15 men and 5 women; age range, 24-34 years) were adept at mentioning dorsal deviation and dorsal profile with focused descriptions of tip angle and contour. Areas commonly omitted by residents included verification of the Frankfort plane, position of the lower lateral crura, radix position, and ratio of the ala to tip lobule. The intervention group demonstrated immediate improvement after instruction on the teaching model, with the mean (SD) postintervention test score doubling compared with their baseline performance (7.5 [2.7] vs 10.3 [2.5]; P < .001). At 10 weeks after the intervention, the mean comparative improvement in overall graded nasal analysis was 17% (95% CI, 10%-23%; P < .001).Conclusions and relevanceOtolaryngology residents demonstrated proficiency at incorporating nasal deviation, tip angle, and dorsal profile contour into their nasal analysis. They often omitted verification of the Frankfort plane, position of lower lateral crura, radix depth, and ala-to-tip lobule ratio. Findings with this novel 10-minute teaching model should be validated at other teaching institutions, and the instruction model should be further enhanced to teach more sophisticated analysis to residents as they proceed through training.Level of evidenceNA.

Project description:BackgroundIn health research, several chronic diseases are susceptible to competing risks (CRs). Initially, statistical models (SM) were developed to estimate the cumulative incidence of an event in the presence of CRs. As recently there is a growing interest in applying machine learning (ML) for clinical prediction, these techniques have also been extended to model CRs but literature is limited. Here, our aim is to investigate the potential role of ML versus SM for CRs within non-complex data (small/medium sample size, low dimensional setting).MethodsA dataset with 3826 retrospectively collected patients with extremity soft-tissue sarcoma (eSTS) and nine predictors is used to evaluate model-predictive performance in terms of discrimination and calibration. Two SM (cause-specific Cox, Fine-Gray) and three ML techniques are compared for CRs in a simple clinical setting. ML models include an original partial logistic artificial neural network for CRs (PLANNCR original), a PLANNCR with novel specifications in terms of architecture (PLANNCR extended), and a random survival forest for CRs (RSFCR). The clinical endpoint is the time in years between surgery and disease progression (event of interest) or death (competing event). Time points of interest are 2, 5, and 10 years.ResultsBased on the original eSTS data, 100 bootstrapped training datasets are drawn. Performance of the final models is assessed on validation data (left out samples) by employing as measures the Brier score and the Area Under the Curve (AUC) with CRs. Miscalibration (absolute accuracy error) is also estimated. Results show that the ML models are able to reach a comparable performance versus the SM at 2, 5, and 10 years regarding both Brier score and AUC (95% confidence intervals overlapped). However, the SM are frequently better calibrated.ConclusionsOverall, ML techniques are less practical as they require substantial implementation time (data preprocessing, hyperparameter tuning, computational intensity), whereas regression methods can perform well without the additional workload of model training. As such, for non-complex real life survival data, these techniques should only be applied complementary to SM as exploratory tools of model's performance. More attention to model calibration is urgently needed.

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Anatomical Models versus Nontactile Distanced Learning in Otolaryngology Teaching.

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