The purpose of this study was to differentiate clinically meaningful improvement or deterioration from normal fluctuations in patients with disorders of consciousness (DoC) following severe brain injury. We computed indices of responsiveness for the Coma Recovery Scale-Revised (CRS-R) using data from a clinical trial of 180 participants with DoC. We used CRS-R scores from baseline (enrollment in a clinical trial) and a 4-week follow-up assessment period for these calculations. To improve precision, we transformed ordinal CRS-R total scores (0-23 points) to equal-interval measures on a 0-100 unit scale using Rasch Measurement theory. Using the 0-100 unit total Rasch measures, we calculated distribution-based 0.5 standard deviation (SD) minimal clinically important difference, minimal detectable change using 95% confidence intervals, and conditional minimal detectable change using 95% confidence intervals. The distribution-based minimal clinically important difference evaluates group-level changes, whereas the minimal detectable change values evaluate individual-level changes. The minimal clinically important difference and minimal detectable change are derived using the overall variability across total measures at baseline and 4 weeks. The conditional minimal detectable change is generated for each possible pair of CRS-R Rasch person measures and accounts for variation in standard error across the scale. We applied these indices to determine the proportions of participants who made a change beyond measurement error within each of the two subgroups, based on treatment arm (amantadine hydrochloride or placebo) or categorization of baseline Rasch person measure to states of consciousness (i.e., unresponsive wakefulness syndrome and minimally conscious state). We compared the proportion of participants in each treatment arm who made a change according to the minimal detectable change and determined whether they also changed to another state of consciousness. CRS-R indices of responsiveness (using the 0-100 transformed scale) were as follows: 0.5SD minimal clinically important difference = 9 units, minimal detectable change = 11 units, and the conditional minimal detectable change ranged from 11 to 42 units. For the amantadine and placebo groups, 70% and 58% of participants showed change beyond measurement error using the minimal detectable change, respectively. For the unresponsive wakefulness syndrome and minimally conscious state groups, 54% and 69% of participants changed beyond measurement error using the minimal detectable change, respectively. Among 115 participants (64% of the total sample) who made a change beyond measurement error, 29 participants (25%) did not change state of consciousness. CRS-R indices of responsiveness can support clinicians and researchers in discerning when behavioral changes in patients with DoC exceed measurement error. Notably, the minimal detectable change can support the detection of patients who make a "true" change within or across states of consciousness. Our findings highlight that the continued use of ordinal scores may result in incorrect inferences about the degree and relevance of a change score.
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http://dx.doi.org/10.1089/neu.2023.0567 | DOI Listing |
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School of Materials and Chemical Engineering, West Anhui University, Lu'an, Anhui, 237012, China.
Nitrogen@Carbon quantum dots (N@CQDs) are prepared using microwave hydrothermal method, and polyvinylpyrrolidone (PVP) and melamine are used as mixed C source and N source. Microwave reaction conditions of preparing the N@CQDs are 170 ℃ and 3 h. This N@CQDs are are used as fluorescence probe for detection of amino acids.
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Florence Nightingale Faculty of Nursing Midwifery & Palliative Care, King's College London, London, UK
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Clin Chem
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Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, United States.
Background: Multianalyte machine learning (ML) models can potentially identify previously undetectable wrong blood in tube (WBIT) errors, improving upon current single-analyte delta check methodology. However, WBIT detection model performance has not been assessed in a real-world, low-prevalence context. To estimate real-world positive predictive values, we propose a methodology to assess WBIT detection models by evaluating the impact of missing data and by using a "low prevalence" validation data set.
View Article and Find Full Text PDFJ Clin Med
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Department of Biomedical Engineering, University of Alberta, Edmonton, AB T6G 2G8, Canada.
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View Article and Find Full Text PDFSensors (Basel)
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Department of Information Technology, Quaid e Awam University, Nawabshah 67450, Pakistan.
Detection of anomalies in video surveillance plays a key role in ensuring the safety and security of public spaces. The number of surveillance cameras is growing, making it harder to monitor them manually. So, automated systems are needed.
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