Medical students' perceptions on preparedness and care delivery for patients with autism or intellectual disability.

Introduction: To provide competent care to patients with autism spectrum disorder (ASD) or intellectual developmental disorder (IDD), healthcare professionals must recognize the needs of neurodivergent populations and adapt their clinical approach. We assessed the perceived preparedness of medical students to adapt care delivery for patients with ASD/IDD, as well as their perceptions on neurodiversity education. Methods: We conducted a sequential explanatory mixed-methods study on undergraduate medical students at McGill University during the academic year 2020-2021. We administered an online survey, followed by semi-structured interviews. We analyzed data using descriptive statistics and thematic analysis. We integrated findings at the interpretation level. Results: We included two-hundred-ten survey responses (~29% of class), and 12 interviews. Few students felt prepared to adjust care for patients with ASD/IDD despite most indicating doing so was important. Ninety-seven percent desired more training regarding care accommodation for neurodivergent patients. Thematic analysis unveiled the perception of current insufficient education, and the value of experiential learning. Discussion/Conclusions: This study highlights low perceived preparedness of medical students to accommodate care for neurodivergent patients, and a desire for more instruction. Incorporating interactive training in medical school curricula regarding modifying care delivery for neurodivergent individuals may improve the perceived preparedness of medical trainees to work with these patients and care quality.

Introduction: Pour fournir des soins compétents aux patients atteints d'un trouble du spectre de l'autisme (TSA) ou d'un trouble du développement intellectuel (TDI), les professionnels de la santé doivent reconnaître les besoins des populations neurodivergentes et adapter leur approche clinique. Nous avons évalué le degré perçu de préparation des étudiants en médecine à adapter la prestation de soins aux patients atteints de TSA/TDI, ainsi que leurs perceptions de la formation relative à la neurodiversité. Méthodes: Nous avons mené une étude séquentielle explicative à méthodes mixtes auprès d'étudiants en médecine de premier cycle de l'Université McGill au cours de l'année universitaire 2020-2021. Nous avons administré un sondage en ligne, suivi d'entretiens semi-structurés. Nous avons analysé les données en faisant appel à la statistique descriptive et à une analyse thématique. Nous avons intégré les résultats sur le plan interprétatif. Résultats: Nous avons inclus deux cent dix réponses à l'enquête (~29% de la classe), et 12 entretiens. Peu d'étudiants se sentaient préparés à adapter les soins aux patients atteints de TSA/TDI, bien que la plupart d'entre eux aient indiqué qu'il était important de le faire. Quatre-vingt-dix-sept pour cent d'entre eux souhaitaient davantage de formation sur l'adaptation des soins aux patients neurodivergents. L'analyse thématique a révélé que les participants considéraient la formation actuelle insuffisante et jugeaient utile l'apprentissage par l'expérience. Discussion/Conclusions: Cette étude met en évidence le faible niveau de préparation perçu par les étudiants en médecine en ce qui concerne l'adaptation des soins aux patients neurodivergents, ainsi que le désir d'une formation plus poussée. L'intégration dans les programmes des facultés de médecine d'une formation interactive portant sur la modification de la prestation des soins pour les personnes neurodivergentes pourrait améliorer la perception des étudiants en médecine de leur degré de préparation à travailler avec ce type de patients et de la qualité des soins.

Multichannel high noise level ECG denoising based on adversarial deep learning.

This paper proposes a denoising method based on an adversarial deep learning approach for the post-processing of multi-channel fetal electrocardiogram (ECG) signals. As it's well known, noise leads to misinterpretations of fetal ECG signals and thus limits the use of fetal electrocardiography for healthcare applications. Therefore, denoising algorithms are essential for the exploitation of non-invasive fetal ECG. The proposed method is based on the combination of three end-to-end trained sub-networks to convert noisy fetal ECG signals into clean signals. The first two sub-networks are linked by skip connections and form a deep convolutional network that downsamples the noisy signals into a latent representation and subsequently upsamples this latent representation to recover clean signals. The third sub-network aims to boost the decoder sub-network to generate realistic clean signals. Experiments carried out on synthetic and real data showed that the proposed method improved by the signal-to-noise (SNR) of fetal ECG signals with input SNR ranging from [Formula: see text] to 0 dB by an average of 20 dB, and improve fetal signal quality by significantly increasing the number of true detected QRS complexes and halving QRS complex detection errors.

Data processing techniques impact quantification of cortico-cortical evoked potentials.

BACKGROUND: Cortico-cortical evoked potentials (CCEPs) are a common tool for probing effective connectivity in intracranial human electrophysiology. As with all human electrophysiology data, CCEP data are highly susceptible to noise. To address noise, filters and re-referencing are often applied to CCEP data, but different processing strategies are used from study to study. NEW METHOD: We systematically compare how common average re-referencing and filtering CCEP data impacts quantification. RESULTS: We show that common average re-referencing and filters, particularly filters that cut out more frequencies, can significantly impact the quantification of CCEP magnitude and morphology. We identify that high cutoff high pass filters (> 0.5 Hz), low cutoff low pass filters (< 200 Hz), and common average re-referencing impact quantification across subjects. However, we also demonstrate that the presence of noise may impact CCEP quantification, and preprocessing is necessary to mitigate this. We show that filtering is more effective than re-referencing or averaging across trials for reducing most common types of noise. COMPARISON WITH EXISTING METHODS: These results suggest that existing CCEP processing methods must be applied with care to maximize noise reduction and minimize changes to the data. We do not test every available processing strategy; rather we demonstrate that processing can influence the results of CCEP studies. We emphasize the importance of reporting all processing methods, particularly re-referencing methods. CONCLUSIONS: We propose a general framework for choosing an appropriate processing pipeline for CCEP data, taking into consideration the noise levels of a specific dataset. We suggest that minimal gentle filtering is preferable.