Purpose: The aim of this systematic scoping review was to explore the use of the motor optimality score in the fidgety movement period in clinical practice, and to investigate evidence for the motor optimality score in predicting neurodevelopmental outcomes.
Summary Of Key Points: Thirty-seven studies, with 3662 infants, were included. Studies were conceptualized and charted into 4 categories based on the motor optimality score: prediction, outcome measure, descriptive, or psychometric properties. The most represented populations were preterm or low-birth-weight infants (16 studies), infants with cerebral palsy or neurological concerns (5 studies), and healthy or term-born infants (4 studies).
Conclusion: The motor optimality score has the potential to add value to existing tools used to predict risk of adverse neurodevelopmental outcomes. Further research is needed regarding the reliability and validity of the motor optimality score to support increased use of this tool in clinical practice. What this adds to the evidence : The motor optimality score has potential to improve the prediction of adverse neurodevelopmental outcomes. Further research on validity and reliability of the motor optimality score is needed; however, a revised version, the motor optimality score-R (with accompanying manual) will likely contribute to more consistency in the reporting of the motor optimality score in future.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1097/PEP.0000000000000969 | DOI Listing |
Sci Rep
January 2025
Department of Neurology, St. Josef-Hospital, Ruhr-University Bochum, Gudrunstr. 56, 44791, Bochum, Germany.
A substantial proportion of patients suffer from Post-COVID Syndrome (PCS) with fatigue and impairment of memory and concentration being the most important symptoms. We here set out to perform in-depth neuropsychological assessment of PCS patients referred to the Neurologic PCS clinic compared to patients without sequelae after COVID-19 (non-PCS) and healthy controls (HC) to decipher the most prevalent cognitive deficits. We included n = 60 PCS patients with neurologic symptoms, n = 15 non-PCS patients and n = 15 healthy controls.
View Article and Find Full Text PDFJ Plast Reconstr Aesthet Surg
December 2024
Division of Plastic and Reconstructive Surgery, Rush University Medical Center, Chicago, IL, United States. Electronic address:
The timing of nerve blocks for amputation surgery with immediate targeted muscle reinnervation (TMR) has been disputed. Traditional practices often defer nerve blocks until post-amputation, fearing interference with motor nerve target identification for TMR. Here, we present a case series demonstrating that pre-amputation regional nerve blocks do not prevent the identification of motor nerve targets.
View Article and Find Full Text PDFAnn Phys Rehabil Med
January 2025
Department of Rehabilitation Medicine, Kyorin University School of Medicine, 6-20-2 Shinkawa, Mitaka-shi, Tokyo, 181-8611, Japan; Department of Rehabilitation Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan. Electronic address:
Background: Despite the lack of clinically validated strategies for treating spinal cord injury (SCI), combining therapeutic strategies with rehabilitation is believed to promote recovery of motor function; however, current research findings are inconsistent.
Objectives: To explore whether combination therapy involving therapy and rehabilitative training (CIRT) has a synergistic effect on motor function recovery in animal models of SCI.
Methods: We conducted a systematic review and meta-analysis of studies identified in a keyword search of 6 databases and extracted open-field motor scores from the Basso Mouse Scale (BMS) and the Basso, Beattie, and Bresnahan Locomotor Rating Scale (BBB) for meta-analysis using a weighted mean difference (WMD) and 95 % CI.
Sensors (Basel)
December 2024
Department of Electronics and Communication Engineering, Istanbul Technical University, 34467 Istanbul, Istanbul, Turkey.
Classifying Motor Imaging (MI) Electroencephalogram (EEG) signals is of vital importance for Brain-Computer Interface (BCI) systems, but challenges remain. A key challenge is to reduce the number of channels to improve flexibility, portability, and computational efficiency, especially in multi-class scenarios where more channels are needed for accurate classification. This study demonstrates that combining Electrooculogram (EOG) channels with a reduced set of EEG channels is more effective than relying on a large number of EEG channels alone.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Computer Science and Engineering, Intelligent Robot Research Institute, Sun Moon University, Asan 31460, Republic of Korea.
This research work presents an integrated method leveraging Convolutional Neural Networks and Recurrent Neural Networks (CNN-RNN) to enhance the accuracy of predictive maintenance and fault detection in DC motor drives of industrial robots. We propose a new hybrid deep learning framework that combines CNNs with RNNs to improve the accuracy of fault prediction that may occur on a DC motor drive during task processing. The CNN-RNN model determines the optimal maintenance strategy based on data collected from sensors, such as air temperature, process temperature, rotational speed, and so forth.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!