Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1097/SLA.0b013e31820dcd34 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Central statistical monitoring in clinical trial management: A scoping review.
Clin Trials
January 2025
Central Monitoring and Data Analytics, GSK, Brentford, UK.
Background: Clinical trials handle a huge amount of data which can be used during the trial to improve the ongoing study conduct. It is suggested by regulators to implement the remote approach to evaluate clinical trials by analysing collected data. Central statistical monitoring helps to achieve that by employing quantitative methods, the results of which are a basis for decision-making on quality issues.
View Article and Find Full Text PDFThe Prognostic Quality of Risk Prediction Models to Assess the Individual Breast Cancer Risk in Women: An Overview of Reviews.
Breast J
January 2025
HTA Austria-Austrian Institute for Health Technology Assessment (AIHTA) GmbH, Garnisongasse 7/21, Vienna 1090, Austria.
Purpose: Breast cancer is the most common cancer among women globally, with an incidence of approximately two million cases in 2018. Organised age-based breast cancer screening programs were established worldwide to detect breast cancer earlier and to reduce mortality. Currently, there is substantial anticipation regarding risk-adjusted screening programs, considering various risk factors in addition to age.
View Article and Find Full Text PDFComparing the mechanical properties of the polymer orthodontic bracket materials with the conventional orthodontic bracket materials: A systematic review.
Dent Res J (Isfahan)
November 2024
Department of Orthodontics and Dentofacial Orthopedics, SRM Dental College and Hospital, Chennai, Tamil Nadu, India.
Background: The aim of this study was to compare the mechanical properties of the polymer brackets with metal and ceramic brackets and verify if the polymer brackets could be used clinically.
Materials And Methods: A thorough search was conducted in four electronic databases, including Scopus, PubMed, Cochrane, Ovid, and Lilacs, with article selection based on Preferred Reporting Items for Systematic Reviews and Meta-Analysis standards. A computerized search of the database was done from January 1990 to June 2024.
Pitfalls in interpreting calibration in comparative evaluations of risk models for precision lung cancer screening.
NPJ Precis Oncol
December 2024
Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Lung cancer screening by low-dose computed tomography reduces lung cancer mortality, but reliable risk-based selection of participants is crucial to maximize benefits and minimize harms. Multiple risk models have been developed for this purpose, and their discrimination and calibration performance is commonly evaluated based on large-scale cohort studies. Using a recent comparative evaluation of 10 risk models as an example, we illustrate the merits, limitations and pitfalls of such evaluations.
View Article and Find Full Text PDFA Machine Learning Risk Prediction Model for Gastric Cancer with SHapley Additive exPlanations.
Cancer Res Treat
December 2024
Division of Gastroenterology, Department of Internal Medicine, Center for Cancer Prevention and Detection, National Cancer Center, Goyang, Korea.
Purpose: Gastric cancer (GC) prediction models hold potential for enhancing early detection by enabling the identification of high-risk individuals, facilitating personalized risk-based screening, and optimizing the allocation of healthcare resources.
Materials And Methods: In this study, we developed a machine learning-based GC prediction model utilizing data from the Korean National Health Insurance Service, encompassing 10,515,949 adults who had not been diagnosed with GC and underwent GC screening during 2013-2014, with a follow-up period of at least five years. The cohort was divided into training and test datasets at an 8:2 ratio, and class imbalance was mitigated through random oversampling.
Want 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!