Objectives: To determine whether the number of words displayed in the word prediction software (WPS) list affects text input speed (TIS) in people with cervical spinal cord injury (SCI), and whether any influence is dependent on the level of the lesion.
Design: A cross-sectional trial.
Setting: A rehabilitation center.
Participants: Persons with cervical SCI (N=45). Lesion level was high (C4 and C5, American Spinal Injury Association [ASIA] grade A or B) for 15 participants (high-lesion group) and low (between C6 and C8, ASIA grade A or B) for 30 participants (low-lesion group).
Intervention: TIS was evaluated during four 10-minute copying tasks: (1) without WPS (Without); (2) with a display of 3 predicted words (3Words); (3) with a display of 6 predicted words (6Words); and (4) with a display of 8 predicted words (8Words).
Main Outcome Measures: During the 4 copying tasks, TIS was measured objectively (characters per minute, number of errors) and subjectively through subject report (fatigue, perception of speed, cognitive load, satisfaction).
Results: For participants with low-cervical SCI, TIS without WPS was faster than with WPS, regardless of the number of words displayed (P<.001). For participants with high-cervical SCI, the use of WPS did not influence TIS (P=.99). There was no influence of the number of words displayed in a word prediction list on TIS; however, perception of TIS differed according to lesion level.
Conclusions: For persons with low-cervical SCI, a small number of words should be displayed, or WPS should not be used at all. For persons with high-cervical SCI, a larger number of words displayed increases the comfort of use of WPS.
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http://dx.doi.org/10.1016/j.apmr.2015.10.080 | DOI Listing |
J Phys Chem Lett
January 2025
College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), State Key Laboratory of Organic Electronics and Information Displays, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210023, P.R. China.
Heat dissipation has become a critical challenge in modern electronics, driving the need for a revolution in thermal management strategies beyond traditional packaging materials, thermal interface materials, and heat sinks. Cubic boron arsenide (c-BAs) offers a promising solution, thanks to its combination of high thermal conductivity and high ambipolar mobility, making it highly suitable for applications in both electronic devices and thermal management. However, challenges remain, particularly in the large-scale synthesis of a high-quality material and the tuning of its physical properties.
View Article and Find Full Text PDFBehav Modif
January 2025
West Virginia University, Morgantown, USA.
The identification of behavioral markers that predict the trajectory of behavior could guide the allocation of limited clinical resources to improve efficacy, efficiency, and safety. As a preliminary exploration of this possibility, we conducted a retrospective records review of incident reports for aggression displayed by residents at a secure juvenile detention center. Our purpose was to evaluate latency to first aggression as a candidate behavioral marker for predicting subsequent high-rate aggression.
View Article and Find Full Text PDFInt J Biol Markers
January 2025
Department of Respiratory and Critical Care Medicine, Anyue County People's Hospital, Anyue, China.
Purpose: To detect the prognostic importance of liquid-liquid phase separation (LLPS) in lung adenocarcinoma.
Methods: The gene expression files, copy number variation data, and clinical data were downloaded from The Cancer Genome Atlas cohort. LLPS-related genes were acquired from the DrLLPS website.
MethodsX
June 2025
Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya 60111 Indonesia.
This research introduces the Generalized Extreme Value Mixture Autoregressive (GEVMAR) model as an innovative approach for examining non-standard actuarial datasets within general insurance. Information concerning claim reserves often reveals notable volatility and multimodal distributions, attributes that standard models, including previous method such as the Gaussian Mixture Autoregressive (GMAR) model and other autoregressive methodologies, find problematic to manage effectively. The GEVMAR model integrates the Generalized Extreme Value (GEV) distribution alongside Bayesian estimation techniques, augmented by a modified Signal-to-Noise Ratio (SNR) metric to improve predictive accuracy.
View Article and Find Full Text PDFTransplantation
January 2025
Department of Thoracic Surgery, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu, China.
Background: Primary graft dysfunction (PGD) develops within 72 h after lung transplantation (Lung Tx) and greatly influences patients' prognosis. This study aimed to establish an accurate machine learning (ML) model for predicting grade 3 PGD (PGD3) after Lung Tx.
Methods: This retrospective study incorporated 802 patients receiving Lung Tx between July 2018 and October 2023 (640 in the derivation cohort and 162 in the external validation cohort), and 640 patients were randomly assigned to training and internal validation cohorts in a 7:3 ratio.
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