Purpose: This study aimed to present an innovative approach to quantify, visualize, and predict radiation therapy (RT) process reliability using data captured from a voluntary incident learning system, with an overall aim to improve patient safety outcomes.
Methods And Materials: We analyzed 111 reported deviations that were tripped and caught within 159 mapped RT process steps included within 7 major stages of RT delivery, 94 of which were any type of quality assurance (QA) controls. This allowed for us to compute the trip rate and fail-to-catch-rate (FCR) per each QA control with the available data. Next, we used a logistic regression model to identify significant variables predictive of FCRs, predicted FCRs for each QA control without available data, and thus, attempted to quantify RT process reliability expressed as percentage of patients with uncaught deviations after treatment planning, before their first treatment, and during treatment delivery.
Results: Using the predicted FCRs, we computed the upper 95% likelihood that a deviation remains uncaught in a patient's course of treatment at the following RT process stages: immediately after treatment planning at 10.26%; before the first treatment at 0.0052%; and throughout treatment delivery at 0.0276%.
Conclusions: The results suggest that RT process reliability can be predicted and visualized using data from incident learning systems. If implemented and used as a safety metric, this could help RT clinics to proactively maintain their preoccupation with patient safety. RT process reliability may also help guide future work on standardization and continuous improvement of the design of RT QA programs.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.prro.2018.11.012 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Fluorofurimazine, a novel NanoLuc substrate, enhances real-time tracking of influenza A virus infection without altering pathogenicity in mice.
Microbiol Spectr
January 2025
Department of Microbiology and Immunology, Frederick P. Whiddon College of Medicine, University of South Alabama, Mobile, Alabama, USA.
Unlabelled: Bioluminescence imaging (BLI) using engineered bioluminescent viruses has emerged as a powerful tool for real-time, noninvasive monitoring of viral replication in living animals. While traditional luciferase-based systems, such as firefly luciferase, have been widely used, the NanoLuc luciferase system offers distinct advantages, including its significantly smaller gene size, increased brightness, and independence from ATP as a cofactor, allowing for extracellular detection. However, the utility of NanoLuc has been limited by its traditional substrate, furimazine, which exhibits poor water solubility and potential cytotoxicity.
View Article and Find Full Text PDFFrom practice to protocol: The CAPE-V by and for Quebec French SLPs.
Clin Linguist Phon
January 2025
École d'orthophonie et d'audiologie, Faculté de médecine, Université de Montréal, Québec, Canada.
This article presents the Quebec French adaptation of the Consensus Auditory-Perceptual Evaluation of Voice (CAPE-V), a standardised protocol for evaluating voice quality. Developed through collaboration within the Quebec Voice Speech-Language Pathologist (SLP) Community of Practice, the adapted tool addresses linguistic and cultural nuances specific to Quebec French. This adaptation ensures standardised assessments and harmonises clinical and research practices across the province.
View Article and Find Full Text PDFIdentifying Primary Sites of Spinal Metastases: Expert-Derived Features vs. ResNet50 Model Using Nonenhanced MRI.
J Magn Reson Imaging
January 2025
Department of Radiology, Peking University Third Hospital, Beijing, China.
Background: The spinal column is a frequent site for metastases, affecting over 30% of solid tumor patients. Identifying the primary tumor is essential for guiding clinical decisions but often requires resource-intensive diagnostics.
Purpose: To develop and validate artificial intelligence (AI) models using noncontrast MRI to identify primary sites of spinal metastases, aiming to enhance diagnostic efficiency.
synthesis of a UIO-66-NH@TiC composite for advanced electrochemical detection of acetaminophen.
Nanoscale
January 2025
Hubei Key Laboratory of Material Chemistry and Service Failure, Key Laboratory of Material Chemistry for Energy Conversion and Storage (Ministry of Education), Hubei Engineering Research Center for Biomaterials and Medical Protective Materials, School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology, 1037 Luoyu Rd, Wuhan, China.
Acetaminophen (AP) is a widely used analgesic and antipyretic drug, but its excessive use poses health risks and contributes to environmental contamination. In response to the need for rapid, accurate, and cost-effective detection methods, we developed a highly sensitive and selective electrochemical sensor for AP. The sensor was based on a composite of UIO-66-NH (UN) and an MXene (TiC).
View Article and Find Full Text PDFFour targets: an enhanced framework for guiding causal inference from observational data.
Int J Epidemiol
December 2024
Program in Addiction Medicine, Yale School of Medicine, New Haven, CT, United States.
Observational studies play an increasingly important role in estimating causal effects of a treatment or an exposure, especially with the growing availability of routinely collected real-world data. To facilitate drawing causal inference from observational data, we introduce a conceptual framework centered around "four targets"-target estimand, target population, target trial, and target validity. We illustrate the utility of our proposed "four targets" framework with the example of buprenorphine dosing for treating opioid use disorder, explaining the rationale and process for employing the framework to guide causal thinking from observational data.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!