Objectives: There is increasing recognition that insufficient attention has been paid to the choice of outcomes measured in clinical trials. The lack of a standardized outcome classification system results in inconsistencies due to ambiguity and variation in how outcomes are described across different studies. Being able to classify by outcome would increase efficiency in searching sources such as clinical trial registries, patient registries, the Cochrane Database of Systematic Reviews, and the Core Outcome Measures in Effectiveness Trials (COMET) database of core outcome sets (COS), thus aiding knowledge discovery.
Study Design And Setting: A literature review was carried out to determine existing outcome classification systems, none of which were sufficiently comprehensive or granular for classification of all potential outcomes from clinical trials. A new taxonomy for outcome classification was developed, and as proof of principle, outcomes extracted from all published COS in the COMET database, selected Cochrane reviews, and clinical trial registry entries were classified using this new system.
Results: Application of this new taxonomy to COS in the COMET database revealed that 274/299 (92%) COS include at least one physiological outcome, whereas only 177 (59%) include at least one measure of impact (global quality of life or some measure of functioning) and only 105 (35%) made reference to adverse events.
Conclusions: This outcome taxonomy will be used to annotate outcomes included in COS within the COMET database and is currently being piloted for use in Cochrane Reviews within the Cochrane Linked Data Project. Wider implementation of this standard taxonomy in trial and systematic review databases and registries will further promote efficient searching, reporting, and classification of trial outcomes.
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http://dx.doi.org/10.1016/j.jclinepi.2017.12.020 | DOI Listing |
J Forensic Leg Med
January 2025
Department of Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Raipur, Chhattisgarh (C.G), India.
Accurate post-mortem interval estimation is crucial in forensic investigations, providing essential information for criminal cases. Traditional techniques frequently encounter inaccuracies stemming from environmental and individual variables. The comet assay is a very sensitive technique that detects DNA damage, which has emerged as a promising tool for assessing DNA degradation.
View Article and Find Full Text PDFBMJ Open
January 2025
Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, Chengdu, Sichuan, China
Background: Kangaroo mother care (KMC) is a care of preterm and low birthweight infants carried skin-to-skin contact with the mother's chest and breastfeeding when possible. KMC has been proven to reduce mortality and morbidity in these infants. However, research on KMC has been limited by significant variability and inconsistency in reported outcomes across studies.
View Article and Find Full Text PDFClin Transl Med
January 2025
Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center of Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Objective: This study investigates the impact of Titin (TTN) gene mutations on radiotherapy sensitivity in rectum adenocarcinoma (READ) by examining changes in the tumour immune microenvironment.
Methods: Data on gene expression and mutations in READ were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Bioinformatics analysis explored the correlation between TTN mutations and immune cell infiltration.
PLoS One
January 2025
Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases Co-constructed by Henan Province & Education Ministry of P.R. China/Henan Key Laboratory of Chinese Medicine for Respiratory Diseases, Henan University of Chinese Medicine, Zhengzhou, China.
Background: With increasing attention to core outcome sets (COS), the number of studies on COS for respiratory diseases (COS-RD) is on the rise. However, the methodological quality is still unclear. Therefore, we conducted a study to assess the methodological quality of studies on COS-RD.
View Article and Find Full Text PDFJ Imaging
December 2024
Department of Mathematics, Universität Innsbruck, Technikerstraße 13, A-6020 Innsbruck, Austria.
Medical image processing has been highlighted as an area where deep-learning-based models have the greatest potential. However, in the medical field, in particular, problems of data availability and privacy are hampering research progress and, thus, rapid implementation in clinical routine. The generation of synthetic data not only ensures privacy but also allows the drawing of new patients with specific characteristics, enabling the development of data-driven models on a much larger scale.
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