The time to acceptance and publication of original research articles in major radiology journals is not affected by their likelihood to change practice, cost to society, or safety concerns.
The time to acceptance and publication of original research articles in major radiology journals is not affected by their likelihood to change practice, cost to society, or safety concerns.
"These disturbing trends may be explained on the basis of low evidence-based medicine levels for validity and strength of most radiology research articles," said lead author Dr. Stephania Rizzo of the University of Milan during a Tuesday scientific session.
Rizzo and colleagues from Massachusetts General Hospital and Harvard Medical School selected 25 consecutive original research articles from each of eight peer-reviewed monthly radiology journals.
North American journals included Radiology, American Journal of Roentgenology, Academic Radiology, and Investigative Radiology. Journals from across the pond were European Radiology, European Journal of Radiology, British Journal of Radiology, and Clinical Radiology.
Two radiologists blinded to journal, author, and affiliation identities graded validity and strength of research articles using the Oxford Center for Evidence-Based Medicine scoring system based on levels of evidence.
Each article was also graded based on likelihood to change current practice and cost to society, need for immediate research, and safety concerns on a five-point scale (1 = no impact, 5 = most impact).
Significant difference in receipt-to-print publication and acceptance-to-print publication was found between different journals. Interestingly, North American authors had longer time to acceptance and publication in the European journals and vice-versa, Rizzo said.
Of particular concern to the researchers was the lack of significant correlation between the evidence-based medicine scores and subjective grading for likelihood to change the current practice or cost and safety concerns and publication times.
Can AI Enhance PET/MRI Assessment for Extraprostatic Tumor Extension in Patients with PCa?
December 17th 2024The use of an adjunctive machine learning model led to 17 and 21 percent improvements in the AUC and sensitivity rate, respectively, for PET/MRI in diagnosing extraprostatic tumor extension in patients with primary prostate cancer.
Can Radiomics Bolster Low-Dose CT Prognostic Assessment for High-Risk Lung Adenocarcinoma?
December 16th 2024A CT-based radiomic model offered over 10 percent higher specificity and positive predictive value for high-risk lung adenocarcinoma in comparison to a radiographic model, according to external validation testing in a recent study.