Objective: Medication errors are currently a worldwide public health issue and it is one of the most serious prescription errors. The objective of the study was to evaluate the practice of prescribing high-alert medications and its association with the prevalence of medication errors in hospital settings.
Methods: A retrospective cross-sectional study was conducted including 4,026 prescription order forms of high-alert medications. There were evaluated all prescriptions received at the pharmacy of a reference hospital in the state of Minas Gerais, southeastern Brazil, over a 30-day period in 2001. Prescription were checked for legibility, patient name, type of prescription, date, handwriting or writing, prescriber identification, drug prescribed, and use of abbreviations. Prescription errors were classified as writing or decision errors and how the type of prescription affected the occurrence of errors was assessed.
Results: Most prescriptions were handwritten (45.7%). In 47.0% of handwritten, mixed and pre-typed prescriptions had patient name errors; the prescriber name was difficult to identify in 33.7%; 19.3% of them were hardly legible or illegible. Of a total of 7,148 high-alert drugs prescribed, 3,177 errors were found, and the most frequent one was missing information (86.5%). Errors occurred mostly in prescriptions of heparin, phentanyl, and midazolam. Intensive care and neurology units had the highest number of errors per prescription. Non-standard abbreviations were frequent and widespread. Overall it was estimated 3.3 errors per prescription order form. Pre-typed prescriptions were less likely to have errors compared to mixed or handwritten prescriptions.
Conclusions: The study results show there is a need for standardizing the prescription process and eliminating handwritten prescriptions. The use of pre-typed or edited prescriptions may reduce errors associated to high-alert medications.
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Nanotechnology
January 2025
Guangdong Detection Center for Microbiology, 100 Xianlie Zhong Road, Guangzhou, 510070, CHINA.
In the published article "Silver nanoparticles directly formed on natural macroporous matrix and their anti-microbial activities, Nanotechnology 18 (2007) 055605", the figure caption of Figure 8 has an error in immersion time, and the correct caption is given in this Corrigendum.
View Article and Find Full Text PDFScience
January 2025
Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA, USA.
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January 2025
Renewable Energy Science and Engineering Department, Faculty of Postgraduate Studies for Advanced Sciences (PSAS), Beni-Suef University, Beni-Suef, Egypt.
This study presents a comprehensive comparative analysis of Machine Learning (ML) and Deep Learning (DL) models for predicting Wind Turbine (WT) power output based on environmental variables such as temperature, humidity, wind speed, and wind direction. Along with Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), the following ML models were looked at: Linear Regression (LR), Support Vector Regressor (SVR), Random Forest (RF), Extra Trees (ET), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). Using a dataset of 40,000 observations, the models were assessed based on R-squared, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).
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January 2025
College of Veterinary Medicine and Animal Science, Federal University of Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil.
This work aimed to evaluate the use of Visible and Near-infrared Spectroscopy (Vis-NIRS) as a tool in the classification of bovine carcasses. A total of 133 animals (77 females, 29 males surgically castrated and 27 males immunologically castrated) were used. Vis-NIRS spectra were collected in a chilling room 24 h postmortem directly on the hanging carcasses over the longissimus thoracis between the surface of the 5th and 6th ribs.
View Article and Find Full Text PDFIn Table 7.2, "Common interfering substances and/or conditions that affect glucose meters (for inpatient and outpatient use)," of the article cited above, the effects on glucose values measured by blood glucose meters for high and low hematocrit were incorrect. For high hematocrit, the effect would be falsely lower blood glucose values.
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