Investigating the impact of remote neuroanatomy education during the COVID-19 pandemic using online examination performance in a National Undergraduate Neuroanatomy Competition.

Neuroanatomy is a notoriously challenging subject for medical students to learn. Due to the coronavirus disease-19 (COVID-19) pandemic, anatomical education transitioned to an online format. We assessed student performance in, and attitudes toward, an online neuroanatomy assessment compared to an in-person equivalent, as a marker of the efficacy of remote neuroanatomy education. Participants in the National Undergraduate Neuroanatomy Competition (NUNC) 2021 undertook two online examinations: a neuroanatomically themed multiple-choice question paper and anatomy spotter. Students completed pre- and post-examination questionnaires to gauge their attitudes toward the online competition and prior experience of online anatomical teaching/assessment. To evaluate performance, we compared scores of students who sat the online (2021) and in-person (2017) examinations, using 12 identical neuroradiology questions present in both years. Forty-six percent of NUNC 2021 participants had taken an online anatomy examination in the previous 12 months, but this did not impact examination performance significantly (p > 0.05). There was no significant difference in examination scores between in-person and online examinations using the 12 neuroradiology questions (p = 0.69). Fifty percent of participants found the online format less enjoyable, with 63% citing significantly fewer networking opportunities. The online competition was less stressful for 55% of participants. This study provides some evidence to suggest that student performance is not affected when undertaking online examinations and proposes that online neuroanatomy teaching methods, particularly for neuroradiology, may be equally as effective as in-person approaches within this context. Participants perceived online examinations as less stressful but raised concerns surrounding the networking potential and enjoyment of online events.

Noninvasive Ventilation in Preterm Infants: Factors Influencing Weaning Decisions and the Role of the Silverman-Andersen Score.

The factors influencing weaning of preterm infants from noninvasive ventilation (NIV) are poorly defined and the weaning decisions are often driven by subjective judgement rather than objective measures. To standardize quantification of respiratory effort, the Silverman-Andersen Score (SAS) was included in our nursing routine. We investigated the factors that steer the weaning process and whether the inclusion of the SAS would lead to more stringent weaning. Following SAS implementation, we prospectively evaluated 33 neonates born ≤ 32 + 0 weeks gestational age. Age-, weight- and sex-matched infants born before routine SAS evaluation served as historic control. In 173 of 575 patient days, NIV was not weaned despite little respiratory distress (SAS ≤ 2), mainly due to bradycardias (60% of days without weaning), occurring alone (40%) or in combination with other factors such as apnea/desaturations. In addition, "soft factors" that are harder to grasp impact on weaning decisions, whereas the SAS overall played a minor role. Consequently, ventilation times did not differ between the groups. In conclusion, NIV weaning is influenced by various factors that override the absence of respiratory distress limiting the predictive value of the SAS. An awareness of the factors that influence weaning decisions is important as prolonged use of NIV has been associated with adverse outcome. Guidelines are necessary to standardize NIV weaning practice.

Prediction models for childhood asthma: A systematic review.

BACKGROUND: The inability to objectively diagnose childhood asthma before age five often results in both under-treatment and over-treatment of asthma in preschool children. Prediction tools for estimating a child's risk of developing asthma by school-age could assist physicians in early asthma care for preschool children. This review aimed to systematically identify and critically appraise studies which either developed novel or updated existing prediction models for predicting school-age asthma. METHODS: Three databases (MEDLINE, Embase and Web of Science Core Collection) were searched up to July 2019 to identify studies utilizing information from children ≤5 years of age to predict asthma in school-age children (6-13 years). Validation studies were evaluated as a secondary objective. RESULTS: Twenty-four studies describing the development of 26 predictive models published between 2000 and 2019 were identified. Models were either regression-based (n = 21) or utilized machine learning approaches (n = 5). Nine studies conducted validations of six regression-based models. Fifteen (out of 21) models required additional clinical tests. Overall model performance, assessed by area under the receiver operating curve (AUC), ranged between 0.66 and 0.87. Models demonstrated moderate ability to either rule in or rule out asthma development, but not both. Where external validation was performed, models demonstrated modest generalizability (AUC range: 0.62-0.83). CONCLUSION: Existing prediction models demonstrated moderate predictive performance, often with modest generalizability when independently validated. Limitations of traditional methods have shown to impair predictive accuracy and resolution. Exploration of novel methods such as machine learning approaches may address these limitations for future school-age asthma prediction.