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Test-Retest Reliability of Mismatch Negativity and Late Discriminative Negativity Response in Children With Listening Difficulties.
Eur J Neurosci
March 2025
School of Psychology, University of Auckland, Auckland, New Zealand.
The aim of the study was to determine the test-retest reliability of MMN and LDN recorded to simple speech contrasts in children with listening difficulties. MMN and LDN responses were recorded from Fz and Cz electrodes for a /da/-/ga/ contrast twice within a 10-day period. To extract MMN and LDN, auditory-evoked responses to /ga/ stimuli presented alone were subtracted from the responses to /ga/ presented within an oddball sequence.
View Article and Find Full Text PDFObjectives: This study aims to explore the perceptions of patients affected by COVID-19 and their families regarding the challenges faced, coping strategies used and lessons learnt in Pakistan.
Design: A qualitative exploratory descriptive approach was used to explore the real-time experiences of the participants.
Setting: The study was carried out in a tertiary care hospital in Karachi, Pakistan.
Artificial intelligence for weight estimation in paediatric emergency care.
BMJ Paediatr Open
March 2025
Biocruces Bizkaia Health Research Institute, Barakaldo, País Vasco, Spain.
Objective: To develop and validate a paediatric weight estimation model adapted to the characteristics of the Spanish population as an alternative to currently extended methods.
Methods: Anthropometric data in a cohort of 11 287 children were used to develop machine learning models to predict weight using height and the body mass index (BMI) quartile (as surrogate for body habitus (BH)). The models were later validated in an independent cohort of 780 children admitted to paediatric emergencies in two other hospitals.
AI-driven Personalized Medicine: Transforming Clinical Practice in Inflammatory Bowel Disease.
Gastroenterology
March 2025
APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland.
Inflammatory bowel disease (IBD) is marked by significant clinical heterogeneity, posing challenges for accurate diagnosis and personalized treatment strategies. Conventional approaches, such as endoscopy and histology, often fail to adequately and accurately predict medium and long-term outcomes, leading to suboptimal patient management. Artificial intelligence (AI) is emerging as a transformative force enabling standardized, accurate, and timely disease assessment and outcome prediction, including therapeutic response.
View Article and Find Full Text PDFArtificial Intelligence: Friend or Foe in the Assessment of Dental Students?
J Dent
March 2025
UCL Eastman Dental Institute, Rockefeller Building, 21 University Street, London, WC1E 6DE, UK.
Objectives: Incorporating artificial intelligence (AI) in assessing dental students' knowledge and skills is in its infancy, despite AI being well established as an aid to aspects of clinical diagnosis and education. This study aimed to investigate whether dental educators perceived AI as beneficial in assessing students.
Methods: This was a mixed methods study where quantitative and qualitative data were generated through a live online polling system, Vevox .
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